An Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation of the ICESat-2 Mission

The Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission has been selected by NASA as a Decadal Survey mission, to be launched in 2016. Mission objectives are to measure land ice elevation, sea ice freeboard/ thickness and changes in these variables and to collect measurements over vegetation that will facilitate determination of canopy height, with an accuracy that will allow prediction of future environmental changes and estimation of sea-level rise. The importance of the ICESat-2 project in estimation of biomass and carbon levels has increased substantially, following the recent cancellation of all other planned NASA missions with vegetation-surveying lidars. Two innovative components will characterize the ICESat-2 lidar: (1) Collection of elevation data by a multi-beam system and (2) application of micropulse lidar (photon counting) technology. A micropulse photon-counting altimeter yields clouds of discrete points, which result from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of returned points to reflectors of interest including canopy and ground in forested areas. The objective of this paper is to derive and validate an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2-type data. Data are based on airborne observations with a Sigma Space micropulse lidar and vary with respect to signal strength, noise levels, photon sampling options and other properties. A mathematical algorithm is developed, using spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors and geostatistical classification parameters and hyperparameters. Validation shows that the algorithm works very well and that ground and canopy elevation, and hence canopy height, can be expected to be observable with a high accuracy during the ICESat-2 mission. A result relevant for instrument design is that even the two weaker beam classes considered can be expected to yield useful results for vegetation measurements (93.01-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp9) and 72.85% - 98.68% for 0.48 msp (msp4)). Resampling options affect results more than noise levels. The algorithm derived here is generally applicable for analysis of micropulse lidar altimeter data collected over forested areas as well as other surfaces, including land ice, sea ice and land surfaces

Algorithm for Detection of Ground and Canopy Cover in Micropulse Photon-Counting Lidar Altimeter Data in Preparation for the ICESat-2 Mission

NASA's Ice, Cloud and Land Elevation Satellite-II (ICESat-2) mission is a decadal survey mission (2016 launch). The mission objectives are to measure land ice elevation, sea ice freeboard, and changes in these variables, as well as to collect measurements over vegetation to facilitate canopy height determination. Two innovative components will characterize the ICESat-2 lidar: 1) collection of elevation data by a multibeam system and 2) application of micropulse lidar (photon-counting) technology. A photon-counting altimeter yields clouds of discrete points, resulting from returns of individual photons, and hence new data analysis techniques are required for elevation determination and association of the returned points to reflectors of interest. The objective of this paper is to derive an algorithm that allows detection of ground under dense canopy and identification of ground and canopy levels in simulated ICESat-2 data, based on airborne observations with a Sigma Space micropulse lidar. The mathematical algorithm uses spatial statistical and discrete mathematical concepts, including radial basis functions, density measures, geometrical anisotropy, eigenvectors, and geostatistical classification parameters and hyperparameters. Validation shows that ground and canopy elevation, and hence canopy height, can be expected to be observable with high accuracy by ICESat-2 for all expected beam energies considered for instrument design (93.01%-99.57% correctly selected points for a beam with expected return of 0.93 mean signals per shot (msp), and 72.85%-98.68% for 0.48 msp). The algorithm derived here is generally applicable for elevation determination from photoncounting lidar altimeter data collected over forested areas, land ice, sea ice, and land surfaces, as well as for cloud detection

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Sub-canopy terrain modelling for archaeological prospecting in forested areas through multiple-echo discrete-pulse laser ranging: a case study from Chopwell Wood, Tyne & Wear

Airborne Light Detection and Ranging (LiDAR) technology is assessed for its effectiveness as a tool for measuring terrain under forest canopy. To evaluate the capability of multiple-return discrete-pulse airborne laser ranging for detecting and resolving sub-canopy archaeological features, LiDAR data were collected from a helicopter over a forest near Gateshead in July 2009. Coal mining and timber felling have characterised Chopwell Wood, a mixed coniferous and deciduous woodland of 360 hectares, since the Industrial Revolution. The state-of-the-art Optech ALTM 3100EA LiDAR system operated at 70,000 pulses per second and raw data were acquired over the study area at a point density of over 30 points per square metre. Reference terrain elevation data were acquired on-site to ‘train’ the progressive densification filtering algorithm of Axelsson (1999; 2000) to identify laser reflections from the terrain surface. A number of sites, offering a variety of tree species, variable terrain roughness & gradient and understorey vegetation cover of varying density, were identified in the wood to assess the accuracy of filtered LiDAR terrain data. Results showed that the laser scanner over-estimated the elevation of reference terrain data by 13±17 cm under deciduous canopy and 23±18 cm under coniferous canopy. Terrain point density was calculated as 4.1 and 2.4 points per square metre under deciduous and coniferous forest, respectively. Classified terrain points were modelled with the kriging interpolation technique and topographic archaeological features, such as coal tubways (transportation routes) and areas of subsidence over relic mine shafts, were identified in digital terrain models (DTMs) using advanced exaggeration and artificial illumination techniques. Airborne LiDAR is capable of recording high quality terrain data even under the most dense forest canopy, but the accuracy and density of terrain data are controlled by a combination of tree species, forest management practices and understorey vegetation

Doctor of Philosophy

dissertationWith increasing wildfire activity throughout the western United States comes an increased need for wildland firefighters to protect civilians, structures, and public resources. In order to mitigate threats to their safety, firefighters employ the use of safety zones (SZ: areas where firefighters are free from harm) and escape routes (ER: pathways for accessing SZ). Currently, SZ and ER are designated by firefighters based on ground-level information, the interpretation of which can be error-prone. This research aims to provide robust methods to assist in the ER and SZ evaluation processes, using remote sensing and geospatial modeling. In particular, I investigate the degree to which lidar can be used to characterize the landscape conditions that directly affect SZ and ER quality. I present a new metric and lidar-based algorithm for evaluating SZ based on zone geometry, surrounding vegetation height, and number of firefighters present. The resulting map contains a depiction of potential SZ throughout Tahoe National Forest, each of which has a value that indicates its wind- and slope-dependent suitability. I then inquire into the effects of three landscape conditions on travel rates for the purpose of developing a geospatial ER optimization model. I compare experimentally-derived travel rates to lidar-derived estimates of slope, vegetation density, and ground surface roughness, finding that vegetation density had the strongest negative effect. Relative travel impedances are then mapped throughout Levan Wildlife Management Area and combined with a route-finding algorithm, enabling the identification of maximally-efficient escape routes between any two known locations. Lastly, I explore a number of variables that can affect the accurate characterization of understory vegetation density, finding lidar pulse density, overstory vegetation density, and canopy height all had significant effects. In addition, I compare two widely-used metrics for understory density estimation, overall relative point density and normalized relative point density, finding that the latter possessed far superior predictive power. This research provides novel insight into the potential use of lidar in wildland firefighter safety planning. There are a number of constraints to widespread implementation, some of which are temporary, such as the current lack of nationwide lidar data, and some of which require continued study, such as refining our ability to characterize understory vegetation conditions. However, this research is an important step forward in a direction that has potential to greatly improve the safety of those who put themselves at risk to ensure the safety of life and property

Analysis of microtopography, vegetation, and active-layer thickness using terrestrial LIDAR and kite photography, Barrow, AK

Arctic regions underlain by permafrost are among the most vulnerable to impacts from climate change. This study examined changes in the active layer of permafrost near Barrow, Alaska at very fine scale to capture subtle changes related to microtopography and landcover. In 2010, terrestrial LIDAR was used to collect high-resolution elevation data for four 10 m × 10 m plots where maximum active-layer thickness (ALT) and elevation have been monitored on an annual basis since the mid-1990s and had been monitored in the 1960s as well. The raw LIDAR point cloud was analyzed and processed into four 10 cm resolution digital elevation models (DEMs). Elevation data, collected using differential global positioning system (DGPS) to assess heave and subsidence, has been gathered annually since 2004 and was used to assess the accuracy of the DEMs generated for August 2010. Higher-resolution DEMs did not have higher accuracy compared to the DGPS control points due to artifacts inherent in the LIDAR data. The four DEMs were used to classify each plot based on microtopographical variations derived from terrain attributes including elevation, slope, and Melton’s Ruggedness Number (MRN). Landcover at each plot was classified using the Visible Vegetation Index (VVI), calculated from a series of high-resolution (~10 cm) kite photographs obtained in August 2012 by researchers from the University of Texas – El Paso. The microtopography and land-cover classifications were then used to analyze ALT and elevation data from a range of years. Analysis revealed little difference in either dataset based upon microtopography and landcover. The high amount of interclass and interannual variation made it difficult to draw any conclusions about temporal trends. The results suggest that while microtopography and vegetation are important factors within the complex interaction which determines ALT, the scale of analysis made possible by the high-resolution data utilized in this study did not significantly enhance understanding of the main controlling mechanisms. While terrestrial LIDAR is excellent for many applications, particularly those with substantial vertical variability, for future research at this scale on relatively flat topography, airborne LIDAR may be more suitable

Characteriation of Mediterranean Aleppo pine forest using low-density ALS data

Los espacios forestales son una fuente de servicios, tanto ambientales como económicos, de gran importancia para la sociedad. La caracterización de estos ambientes ha requerido tradicionalmente de un laborioso trabajo de campo. La aplicación de técnicas de teledetección ha proporcionado una visión más amplia a escala espacial y temporal, a la par que ha generado una reducción de los costes. La utilización de sensores óptico-pasivo multiespectrales y de sensores radar posibilita la estimación de parámetros forestales, si bien el desarrollo de sensores LiDAR, como el caso de los escáneres láser aeroportados (ALS), ha mejorado la caracterización tridimensional de la estructura de los bosques. La disponibilidad pública de dos coberturas LiDAR, generadas en el marco del Plan Nacional de Ortofotografía Aérea (PNOA), ha abierto nuevas líneas de investigación que permiten proporcionar información útil para la gestión forestal. La presente tesis utiliza datos LiDAR aeroportados de baja densidad para estimar diversas variables forestales, con ayuda de trabajo de campo, en masas forestales de Pino carrasco (Pinus halepensis Miller) en Aragón. La investigación aborda dos cuestiones relevantes como son la exploración de las metodologías más adecuadas para estimar variables forestales considerando escalas locales y regionales, teniendo en cuenta las posibles fuentes de error en el modelado; y, además, analiza la potencialidad de los datos LiDAR del PNOA para el desarrollo de aplicaciones forestales que valoricen las áreas forestales como recursos socio-económicos. La tesis se ha desarrollado según la modalidad de compendio de publicaciones, incluyendo cuatro trabajos que dan respuesta a los objetivos planteados. En primer lugar, se realiza un análisis comparativo de distintos modelos de regresión, paramétricos y no paramétricos, para estimar la pérdida de biomasa y las emisiones de CO2 en un incendio, mediante la utilización de datos LiDAR-PNOA y datos ópticos del satélite Landsat 8. En segundo lugar, se explora la idoneidad de distintos métodos de selección de variables para estimar biomasa total en masas de Pino carrasco utilizando datos LiDAR de baja densidad. En tercer lugar, se cuantificó y cartografió la biomasa residual forestal en el conjunto de masas de Pino carrasco de Aragón y se evaluó el efecto de diversas características de la tecnología LiDAR y de las variables ambientales en la precisión de los modelos. Finalmente, se analiza la transferibilidad temporal de modelos para estimar a escala regional siete variables forestales, utilizando datos LiDAR-PNOA multi-temporales. A este respecto, se compararon dos enfoques que permiten analizar la transferibilidad temporal: en primer lugar, el método directo ajusta un modelo para un determinado punto en el tiempo y estima las variables forestales para otra fecha; por otra parte, el método indirecto ajusta dos modelos diferentes para cada momento en el tiempo, estimando las variables forestales en dos fechas distintas. Los resultados obtenidos y las conclusiones derivadas de la investigación indican que la técnica basada en coeficientes de correlación de Spearman y el método de selección por todos los subconjuntos constituyen los métodos de selección de métricas LiDAR más apropiados para la modelización. El análisis de métodos de regresión para la estimación de variables forestales indicó que su idoneidad variaba de acuerdo con el tamaño y complejidad de la muestra. El método de regresión linear multivariante arrojó mejores resultados que los métodos no-paramétricos en el caso de muestras pequeñas. Por el contrario, el método Support Vector Machine produjo los mejores resultados con muestras grandes. El incremento de la densidad de puntos y de los valores de penetración de los pulsos LiDAR en el dosel, así como la presencia de ángulos de escaneo pequeños, incrementó la exactitud de los modelos. De forma similar, el incremento de la pendiente y la presencia de arbustos en el sotobosque implican una reducción en la exactitud de los modelos. En la estimación de variables forestales utilizando datos LiDAR multi-temporales, aunque la utilización del enfoque indirecto arrojó generalmente una mayor precisión en los modelos, se obtuvieron resultados similares con el enfoque directo, el cual constituye una alternativa óptima para reducir el tiempo de modelado y los costes de realización de trabajo de campo. La fusión de datos LiDAR y datos óptico-pasivos ha evidenciado la conveniencia de los métodos aplicados para cuantificar las emisiones de CO2 a la atmósfera generadas por un incendio. Esta metodología constituye una alternativa adecuada cuando no existen datos multi-temporales LiDAR. La estimación de variables de inventario forestal, así como de diversas fracciones de biomasa, como la biomasa total y la biomasa residual forestal, proporciona información valiosa para caracterizar las masas forestales mediterráneas de Pino carrasco y mejorar la gestión forestalForest ecosystems provide environmental and economic services of great importance to the society. The characterization of these environments has been traditionally accomplished with intense field work. In comparison, the application of remote sensing tools provides a greater overview over large spatial and temporal scales while minimizing costs. Although optical data and Synthetic Aperture Radar (SAR) allow estimating forest stand variables, the development of LiDAR sensors such as Airborne Laser Scanner (ALS) have improved three-dimensional characterization of forest structure. The availability of two ALS public data coverages for the Spanish territory, provided by the National Plan for Aerial Ortophotography (PNOA), opens new research opportunities to generate useful information for forest management. This PhD Thesis used low-density ALS-PNOA data to estimate different forest variables, with support in fieldwork, in the Aleppo pine (Pinus halepensis Miller) forests of Aragón region. The addressed research is relevant mainly for two reasons: first, the examination of suitable methodologies and error sources in forest stand variables prediction at local (small area) and regional scales (large area), and second, the application of ALS data to the characterization of forest areas as a socio-economic reservoir. This PhD Thesis is a compendium of four scientific papers, which sequentially answer the objectives established. Firstly, a comparative analysis of different parametric and non-parametric models was performed to estimate biomass losses and CO2 emissions using low-density ALS and Landsat 8 data in a burnt Aleppo pine forest. Secondly, we assess the suitability of variable selection methods when estimating total biomass in Aleppo pine forest stands using low-density ALS data. In the third manuscript, the quantification and mapping of forest residual biomass in Aleppo pine forest of Aragón region and the assessment of the effect of ALS and environmental variables in model accuracy were accomplished. Finally, the temporal transferability of seven forest stands attributes modelling using multi-temporal ALS-PNOA data in Aleppo pine forest at regional scale was explored. In this case, the temporal transferability was assessed comparing two methodologies; the direct and indirect approach. The first one fits a model for one point in time and estimates the forest variable for another point in time. The indirect approach adjusts two models in different points in time to estimate the forest variables in two different dates. The results derived from this research indicated that Spearman’s rank and All Subset Selection are the most appropriate methods in the ALS metrics selection step commonly applied in modelling. The suitability of the regression methods depends on the sample size and complexity. Thus, multivariate linear regression outperformed non-parametric methods with small samples while support vector machine was the most accurate method with larger samples. Model accuracy increased with higher point density and canopy pulse penetration, while decreasing with wider scan angles. Furthermore, the presence of steep slopes and shrub reduced model performance. In the case of forest stand variables prediction using multi-temporal ALS data, although the indirect approach produced generally a higher precision, the direct approach provided similar results, constituting a suitable alternative to reduce modelling time and fieldwork costs. The fusion of ALS and passive optical data have evidenced the suitability of this information for quantifying wildfire CO2 emissions to atmosphere, constituting a good alternative when multi-temporal ALS data is not available. The estimation of forest inventory variables as well as different biomass fractions, such as total biomass and forest residual biomass, provided valuable information to characterize Mediterranean Aleppo pine forests and improve forest management.<br /

Caractérisation spatio-temporelle de la dynamique des trouées et de la réponse de la forêt boréale à l'aide de données lidar multi-temporelles

La forêt boréale est un écosystème hétérogène et dynamique façonné par les perturbations naturelles comme les feux, les épidémies d'insectes, le vent et la régénération. La dynamique des trouées joue un rôle important dans la dynamique forestière parce qu'elle influence le recrutement de nouveaux individus au sein de la canopée et la croissance de la végétation avoisinante par une augmentation des ressources. Bien que l'importance des trouées en forêt boréale fut reconnue, les connaissances nécessaires à la compréhension des relations entre le régime de trouées et la dynamique forestière, en particulier sur la croissance, sont souvent manquantes. Il est difficile d'observer et de mesurer extensivement la dynamique des trouées ou les changements de la canopée simultanément dans le temps et l'espace avec des données terrain ou des images bidimensionnelles (photos aériennes,...) et ce particulièrement dans des systèmes complexes comme les forêts ouvertes ou morcelées. De plus, la plupart des recherches furent menées en s'appuyant sur seulement quelques trouées représentatives bien que les interactions entre les trouées et la structure forestière furent rarement étudiées de manière conjointe. Le lidar est un système qui balaye la surface terrestre avec des faisceaux laser permettant d'obtenir une image dense de points en trois dimensions montrant les aspects structuraux de la végétation et de la topographie sous-jacente d'une grande superficie. Nous avons formulé l'hypothèse que lorsque les retours lidar de tirs quasi-verticaux sont denses et précis, ils permettent une interprétation de la géométrie des trouées et la comparaison de celles-ci dans le temps, ce qui nous informe à propos de leur influence sur la dynamique forestière. De plus, les mesures linéaires prises à différents moments dans le temps permettraient de donner une estimation fiable de la croissance. Ainsi, l'objectif de cette recherche doctorale était de développer des méthodes et d'accroître nos connaissances sur le régime de trouées et sa dynamique, et de déterminer comment la forêt boréale mixte répond à ces perturbations en termes de croissance et de mortalité à l'échelle locale. Un autre objectif était aussi de comprendre le rôle à court terme des ouvertures de la canopée dans un peuplement et la dynamique successionelle. Ces processus écologiques furent étudiés en reconstituant la hauteur de la surface de la canopée de la forêt boréale par l'utilisation de données lidar prises. en 1998, 2003 (et 2007), mais sans spécifications d'études similaires. L'aire d'étude de 6 km² dans la Forêt d'Enseignement et de Recherche du Lac Duparquet, Québec, Canada, était suffisamment grande pour capter la variabilité de la structure de la canopée et de la réponse de la forêt à travers une gamme de peuplements à différents stades de développement. Les recherches menées lors de cette étude ont révélé que les données lidar multi-temporelles peuvent être utilisées a priori dans toute étude de télédétection des changements, dont l'optimisation de la résolution des matrices et le choix de l'interpolation des algorithmes sont essentiels (pour les surfaces végétales et terrestres) afin d'obtenir des limites précises des trouées. Nous avons trouvé qu'une technique basée sur la croissance de régions appliquée à une surface lidar peut être utilisée pour délimiter les trouées avec une géométrie précise et pour éliminer les espaces entre les arbres représentant de fausses trouées. La comparaison de trouées avec leur délimitation Iidar le long de transects linéaires de 980 mètres montre une forte correspondance de 96,5%. Le lidar a été utilisé avec succès pour délimiter des trouées simples (un seul arbre) ou multiples (plus de 5 m²). En utilisant la combinaison de séries temporelles de trouées dérivées du lidar, nous avons développé des méthodes afin de délimiter les divers types d'évènements de dynamique des trouées: l'occurrence aléatoire de trouées, l'expansion de trouées et la fermeture de trouées, tant par la croissance latérale que la régénération. La technique proposée pour identifier les hauteurs variées arbre/gaulis sur une image lidar d'un Modèle de Hauteur de Couvert (MHC) a montré près de 75 % de correspondance avec les localisations photogrammétriques. Les taux de croissance libre suggérés basés sur les donnés lidar brutes après l'élimination des sources possibles d'erreur furent utilisés subséquemment pour des techniques statistiques afin de quantifier les réponses de croissance en hauteur qui ont été trouvées afin de faire varier la localisation spatiale en respect de la bordure de la trouée. À partir de la combinaison de donnés de plusieurs groupes d'espèces (de conifères et décidues) interprétée à partir d'images à haute résolution avec des données structurales lidar nous avons estimé les patrons de croissance en hauteur des différents groupes arbres/gaulis pour plusieurs contextes de voisinage. Les résultats on montré que la forêt boréale mixte autour du lac Duparquet est un système hautement dynamique, où la perturbation de la canopée joue un rôle important même pour une courte période de temps. La nouvelle estimation du taux de formation des trouées était de 0,6 %, ce qui correspond à une rotation de 182 ans pour cette forêt. Les résultats ont montré aussi que les arbres en périphérie des trouées étaient plus vulnérables à la mortalité que ceux à l'intérieur du couvert, résultant en un élargissement de la trouée. Nos résultats confirment que tant la croissance latérale que la croissance en hauteur de la régénération contribuent à la fermeture de la canopée à un taux annuel de 1,2 %. Des évidences ont aussi montré que les trouées de conifères et de feuillus ont des croissances latérales (moyenne de 22 cm/an) et verticales similaires sans tenir compte de leur localisation et leur hauteur initiale. La croissance en hauteur de tous les gaulis était fortement positive selon le type d'évènement et la superficie de la trouée. Les résultats suggèrent que la croissance des gaulis de conifères et de feuillus atteint son taux de croissance maximal à des distances respectives se situant entre 0,5 et 2 m et 1,5 et 4 m à partir de la bordure d'une trouée et pour des ouvertures de moins de 800 m² et 250 m² respectivement. Les effets des trouées sur la croissance en hauteur d'une forêt intacte se faisaient sentir à des distance allant jusqu'à à 30 m et 20 m des trouées, respectivement pour les feuillus et les conifères. Des analyses fines de l'ouverture de la canopée montrent que les peuplements à différents stades de développement sont hautement dynamiques et ne peuvent systématiquement suivre les mêmes patrons successionels. Globalement, la forêt est presqu'à l'équilibre compositionnel avec une faible augmentation de feuillus, principalement dû à la régénération de type infilling plutôt qu'une transition successionelle de conifères tolérants à l'ombre. Les trouées sont importantes pour le maintien des feuillus puisque le remplacement en sous-couvert est vital pour certains résineux. L'étude à démontré également que la dernière épidémie de tordeuse des bourgeons de l'épinette qui s'est terminée il y a 16 ans continue d'affecter de vieux peuplements résineux qui présentent toujours un haut taux de mortalité. Les résultats obtenus démontrent que lidar est un excellent outil pour acquérir des détails rapidement sur les dynamiques spatialement extensives et à court terme des trouées de structures complexes en forêt boréale. Les évidences de cette recherche peuvent servir tant à l'écologie, la sylviculture, l'aménagement forestier et aux spécialistes lidar. Ces idées ajoutent une nouvelle dimension à notre compréhension du rôle des petites perturbations et auront une implication directe pour les aménagistes forestiers en quête d'un aménagement forestier écologique et du maintien des forêts mixtes. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Perturbation naturelle, Dynamique forestière, Dynamique des trouées, Croissances latérales, Régénération, Succession, Lidar à retours discrets, Grande superficie, Localisation des arbres individuels, Croissance en hauteur

Delineation of Karst Potential Using LiDAR and GIS Analyses, Fort Hood Military Installation, Texas

ABSTRACT Traditional karst surveys require extensive field investigations to completely characterize large areas. They are often time-consuming, requiring up to several years to collect and categorize data. Bias is given to areas that are most easily accessible and false negatives are common. The implementation of geographic information systems (GIS) has aided in the aggregation and standardization of karst data; however, these systems have also been used to develop terrain models that allow the user to remotely delineate sinkholes and other surficial features. The Fort Hood Military Installation is a karst landscape that has been altered significantly for use in military training exercises. The ground surface is covered with karst features that are environmentally and structurally sensitive to surrounding activity. These manifest primarily as sinks, pits, and caves, which are typically less than a few meters in diameter or depth. Previous speleological studies in this area have understated the amount and spatial distribution of karst, particularly in western Fort Hood. The following approach uses LiDAR (Light Detection and Ranging) data to provide a more complete karst inventory for the Shell Mountain, Manning Mountain and Royalty Ridge provinces. Data was processed using a digital elevation model (DEM) derived from LiDAR to automatically fill and extract areas with localized depressions at sub-meter scale. The resulting polygons were processed through a series of filters that isolated depressions outside the influence of non-karst features and with a depth greater than the vertical accuracy of the LiDAR survey. A karst potential map was produced to characterize the remaining depressions into areas of high and low karst density. Potential sinks are distributed across positive relief features in clusters. Their morphology supports a duality of dissolution and collapse origins. Close comparison with manual surveys and field verification points showed that the results were accurate, if not slightly overestimated. These models will be used to aid future investigations and land use planning at Fort Hood