Assesment of biomass and carbon dynamics in pine forests of the Spanish central range: A remote sensing approach

Forests play a dynamic role in the terrestrial carbon (C) budget, by means of the biomass stock and C fluxes involved in photosynthesis and respiration. Remote sensing in combination with data analysis constitute a practical means for evaluation of forest implications in the carbon cycle, providing spatially explicit estimations of the amount, quality, and spatio-temporal dynamics of biomass and C stocks. Medium and high spatial resolution optical data from satellite-borne sensors were employed, supported by field measures, to investigate the carbon role of Mediterranean pines in the Central Range of Spain during a 25 year period (1984-2009). The location, extent, and distribution of pine forests were characterized, and spatial changes occurred in three sub-periods were evaluated. Capitalizing on temporal series of spectral data from Landsat sensors, novel techniques for processing and data analysis were developed to identify successional processes at the landscape level, and to characterize carbon stocking condition locally, enabling simultaneous characterization of trends and patterns of change. High spatial resolution data captured by the commercial satellite QuickBird-2 were employed to model structural attributes at the stand level, and to explore forest structural diversity

Fourth Airborne Geoscience Workshop

The focus of the workshop was on how the airborne community can assist in achieving the goals of the Global Change Research Program. The many activities that employ airborne platforms and sensors were discussed: platforms and instrument development; airborne oceanography; lidar research; SAR measurements; Doppler radar; laser measurements; cloud physics; airborne experiments; airborne microwave measurements; and airborne data collection

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Méthodes d'analyse de texture pour la cartographie d'occupations du sol par télédetection très haute résolution : application à la fôret, la vigne et les parcs ostréicoles

The prime objective of this thesis is to evaluate the potential of multivariate probabilistic models applied on wavelet subbands for the classification of very high resolution remote sensing optical data. Three main applications are investigated in this study: the differentiation of age classes of maritime pine forest stands, the detection of vineyards and the detection of oyster fields. One main contribution includes the proposal of an original supervised and object-oriented classification scheme based on similarity measurements adapted to the context of probabilistic modeling. This scheme involves the creation of a database of texture patches for the learning step and a pre-segmentation of the image to classify. The tested multivariate models were first evaluated in an image retrieval framework. The best models identified in this procedure were then applied in the proposed image processing scheme. In the three proposed thematic applications, multivariate models revealed remarkable abilities to represent the texture and reached higher classification accuracies than the method based on co-occurrence matrices. These results confirm the interest of the multi-scale and multi-orientation representation of textures through the wavelet transform, as well as the relevance of the multivariate modeling of wavelet coefficientsLe travail présenté dans cette thèse a pour objectif d’évaluer le potentiel de modèles probabilistes multivariés appliqués sur les sous-bandes d’une décomposition en ondelettes pour la classification d’images de télédétection optiques à très haute résolution spatiale. Trois thématiques principales ont été investiguées dans ce travail : la différenciation de classes d’âge de peuplements de pins maritimes, la détection de parcelles viticoles et la détection de parcs ostréicoles. Une contribution originale concerne la proposition d’une chaîne traitement pour une classification supervisée orientée objet se basant sur des mesures de similarité adaptées au contexte de modélisation probabiliste. Celle-ci implique la création d’une base de données de patchs de texture pour l’apprentissage et l’utilisation d’une pré-segmentation de l’image à classifier. Les modèles probabilistes multivariés testés ont tout d’abord été évalués dans une procédure d’indexation d’images. Les modèles les plus performants identifiés par cette procédure ont été ensuite appliqués dans la chaîne de traitement proposée. Dans les trois thématiques explorées, les modèles multivariés ont révélé des capacités remarquables de représentation de la texture et ont permis d’obtenir une qualité de classification supérieure à celle obtenue par la méthode des matrices de co-occurrence. Ces résultats démontrent l’intérêt de la représentation multi-échelles et multi-orientations de la texture dans l’espace transformé en ondelettes et la pertinence de la modélisation multivariée des coefficients d’ondelettes issus de cette décomposition

Caractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale

Very High spatial Resolution (VHR) images like Pléiades imagery (50 cm panchromatic, 2m multispectral) allows a detailed description of forest structure (tree distribution and size) at stand level, by exploiting the spatial relationship between tree structure and image texture when the pixel size is smaller than tree dimensions. This information meets the expected strong need for spatial inventory of forest resources at the stand level and its changes due to forest management, land use or catastrophic events. The aim is twofold : (1) assess the VHR satellite images potential to estimate the main variables of forest structure from the image texture: crown diameter, stem diameter, height, density or tree spacing, (2) on these bases, a pixel-based image classification of forest structure is processed in order to produce the finest possible spatial information. The main developments concern parameter optimization, variable selection, multivariate regression modelling and ensemble-based classification (Random Forests). They are tested and evaluated on the Landes maritime pine forest with three Pléiades images and a Quickbird image acquired under different conditions (season, sun angle, view angle). The method is generic. The robustness of the proposed method to image acquisition parameters is evaluated. Results show that fine variations of texture characteristics related to those of forest structure are clearly identifiable. Performances in terms of forest variable estimation (RMSE): ~1,1m for crown diameter, ~3m for tree height and ~0,9m for tree spacing, as well as forest structure mapping (~82% Overall accuracy for the classification of the five main forest structure classes) are satisfactory from an operational perspective. Their application to multi- annual images will assess their ability to detect and map forest changes such as clear cut, urban sprawl or storm damages.Les images à très haute résolution spatiale (THR) telles que les images Pléiades (50 cm en Panchromatique, 2m en multispectral) rendent possible une description fine de la structure forestière (distribution et dimensions des arbres) à l'échelle du peuplement, en exploitant la relation entre la structure spatiale des arbres et la texture d'image quand la taille du pixel est inférieure à la dimension des arbres. Cette attente répond au besoin d'inventaire spatialisé de la ressource forestière à l'échelle du peuplement et de ses changements dus à la gestion forestière, à l'aménagement du territoire ou aux événements catastrophiques. L'objectif est double: (1) évaluer le potentiel de la texture d'images THR pour estimer les principales variables de structure forestière (diamètre des couronnes, diamètre du tronc, hauteur, densité ou espacement des arbres) à l'échelle du peuplement; (2) sur ces bases, classer les données image, au niveau pixel, par types de structure forestière afin de produire l'information spatialisée la plus fine possible. Les principaux développements portent sur l'automatisation du paramètrage, la sélection de variables, la modélisation par régression multivariable et une approche de classification par classifieurs d'ensemble (Forêts Aléatoires ou Random Forests). Ils sont testés et évalués sur deux sites de la forêt landaise de pin maritime à partir de trois images Pléiades et une Quickbird, acquises dans diverses conditions (saison, position du soleil, angles de visée). La méthodologie proposée est générique. La robustesse aux conditions d'acquisition des images est évaluée. Les résultats montrent que des variations fines de texture caractéristiques de celles de la structure forestière sont bien identifiables. Les performances en terme d'estimation des variables forestières (RMSE) : ~1.1 m pour le diamètre des couronnes, ~3 m pour la hauteur des arbres ou encore ~0.9 m pour leur espacement, ainsi qu'en cartographie des structures forestières (~82 % de taux de bonne classification pour la reconnaissance des 5 classes principales de la structure forestière) sont satisfaisantes d'un point de vue opérationnel. L'application à des images multi-annuelles permettra d'évaluer leur capacité à détecter et cartographier des changements tels que coupe forestière, mitage urbain ou encore dégâts de tempête

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

Biomass Representation in Synthetic Aperture Radar Interferometry Data Sets

This work makes an attempt to explain the origin, features and potential applications of the elevation bias of the synthetic aperture radar interferometry (InSAR) datasets over areas covered by vegetation. The rapid development of radar-based remote sensing methods, such as synthetic aperture radar (SAR) and InSAR, has provided an alternative to the photogrammetry and LiDAR for determining the third dimension of topographic surfaces. The InSAR method has proved to be so effective and productive that it allowed, within eleven days of the space shuttle mission, for acquisition of data to develop a three-dimensional model of almost the entire land surface of our planet. This mission is known as the Shuttle Radar Topography Mission (SRTM). Scientists across the geosciences were able to access the great benefits of uniformity, high resolution and the most precise digital elevation model (DEM) of the Earth like never before for their a wide variety of scientific and practical inquiries. Unfortunately, InSAR elevations misrepresent the surface of the Earth in places where there is substantial vegetation cover. This is a systematic error of unknown, yet limited (by the vertical extension of vegetation) magnitude. Up to now, only a limited number of attempts to model this error source have been made. However, none offer a robust remedy, but rather partial or case-based solutions. More work in this area of research is needed as the number of airborne and space-based InSAR elevation models has been steadily increasing over the last few years, despite strong competition from LiDAR and optical methods. From another perspective, however, this elevation bias, termed here as the “biomass impenetrability”, creates a great opportunity to learn about the biomass. This may be achieved due to the fact that the impenetrability can be considered a collective response to a few factors originating in 3D space that encompass the outermost boundaries of vegetation. The biomass, presence in InSAR datasets or simply the biomass impenetrability, is the focus of this research. The report, presented in a sequence of sections, gradually introduces terminology, physical and mathematical fundamentals commonly used in describing the propagation of electromagnetic waves, including the Maxwell equations. The synthetic aperture radar (SAR) and InSAR as active remote sensing methods are summarised. In subsequent steps, the major InSAR data sources and data acquisition systems, past and present, are outlined. Various examples of the InSAR datasets, including the SRTM C- and X-band elevation products and INTERMAP Inc. IFSAR digital terrain/surface models (DTM/DSM), representing diverse test sites in the world are used to demonstrate the presence and/or magnitude of the biomass impenetrability in the context of different types of vegetation – usually forest. Also, results of investigations carried out by selected researchers on the elevation bias in InSAR datasets and their attempts at mathematical modelling are reviewed. In recent years, a few researchers have suggested that the magnitude of the biomass impenetrability is linked to gaps in the vegetation cover. Based on these hints, a mathematical model of the tree and the forest has been developed. Three types of gaps were identified; gaps in the landscape-scale forest areas (Type 1), e.g. forest fire scares and logging areas; a gap between three trees forming a triangle (Type 2), e.g. depending on the shape of tree crowns; and gaps within a tree itself (Type 3). Experiments have demonstrated that Type 1 gaps follow the power-law density distribution function. One of the most useful features of the power-law distributed phenomena is their scale-independent property. This property was also used to model Type 3 gaps (within the tree crown) by assuming that these gaps follow the same distribution as the Type 1 gaps. A hypothesis was formulated regarding the penetration depth of the radar waves within the canopy. It claims that the depth of penetration is simply related to the quantisation level of the radar backscattered signal. A higher level of bits per pixels allows for capturing weaker signals arriving from the lower levels of the tree crown. Assuming certain generic and simplified shapes of tree crowns including cone, paraboloid, sphere and spherical cap, it was possible to model analytically Type 2 gaps. The Monte Carlo simulation method was used to investigate relationships between the impenetrability and various configurations of a modelled forest. One of the most important findings is that impenetrability is largely explainable by the gaps between trees. A much less important role is played by the penetrability into the crown cover. Another important finding is that the impenetrability strongly correlates with the vegetation density. Using this feature, a method for vegetation density mapping called the mean maximum impenetrability (MMI) method is proposed. Unlike the traditional methods of forest inventories, the MMI method allows for a much more realistic inventory of vegetation cover, because it is able to capture an in situ or current situation on the ground, but not for areas that are nominally classified as a “forest-to-be”. The MMI method also allows for the mapping of landscape variation in the forest or vegetation density, which is a novel and exciting feature of the new 3D remote sensing (3DRS) technique. Besides the inventory-type applications, the MMI method can be used as a forest change detection method. For maximum effectiveness of the MMI method, an object-based change detection approach is preferred. A minimum requirement for the MMI method is a time-lapsed reference dataset in the form, for example, of an existing forest map of the area of interest, or a vegetation density map prepared using InSAR datasets. Preliminary tests aimed at finding a degree of correlation between the impenetrability and other types of passive and active remote sensing data sources, including TerraSAR-X, NDVI and PALSAR, proved that the method most sensitive to vegetation density was the Japanese PALSAR - L-band SAR system. Unfortunately, PALSAR backscattered signals become very noisy for impenetrability below 15 m. This means that PALSAR has severe limitations for low loadings of the biomass per unit area. The proposed applications of the InSAR data will remain indispensable wherever cloud cover obscures the sky in a persistent manner, which makes suitable optical data acquisition extremely time-consuming or nearly impossible. A limitation of the MMI method is due to the fact that the impenetrability is calculated using a reference DTM, which must be available beforehand. In many countries around the world, appropriate quality DTMs are still unavailable. A possible solution to this obstacle is to use a DEM that was derived using P-band InSAR elevations or LiDAR. It must be noted, however, that in many cases, two InSAR datasets separated by time of the same area are sufficient for forest change detection or similar applications

Climate-Smart Forestry in Mountain Regions

This open access book offers a cross-sectoral reference for both managers and scientists interested in climate-smart forestry, focusing on mountain regions. It provides a comprehensive analysis on forest issues, facilitating the implementation of climate objectives. This book includes structured summaries of each chapter. Funded by the EU’s Horizon 2020 programme, CLIMO has brought together scientists and experts in continental and regional focus assessments through a cross-sectoral approach, facilitating the implementation of climate objectives. CLIMO has provided scientific analysis on issues including criteria and indicators, growth dynamics, management prescriptions, long-term perspectives, monitoring technologies, economic impacts, and governance tools

Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires Advances in Remote Sensing and GIS Applications in Forest Fire Management Towards an Operational Use of Remote Sensing in Forest Fire Management

During the last two decades, interest in forest fire research has grown steadily, as more and more local and global impacts of burning are being identified. The definition of fire regimes as well as the identification of factors explaining spatial and temporal variations in these fire characteristics are recently hot fields of research. Changes in these fire regimes have important social and ecological implications. Whether these changes are mainly caused by land use or climate warming, greater efforts are demanded to manage forest fires at different temporal and spatial scales. The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying Mediterranean fires in Europe. It has promoted five technical meetings and several specialised publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of fire risk estimation and the assessment of fire effect. The aim of the 6th international workshop is to analyze the operational use of remote sensing in forest fire management, bringing together scientists and fire managers to promote the development of methods that may better serve the operational community. This idea clearly links with international programmes of a similar scope, such as the Global Monitoring for Environment and Security (GMES) and the Global Observation of Forest Cover/Land Dynamics (GOFC-GOLD) who, together with the Joint Research Center of the European Union sponsor this event. Finally, I would like to thank the local organisers for the considerable lengths they have gone to in order to put this material together, and take care of all the details that the organization of this event requires.JRC.H.3-Global environement monitorin

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry