Bridging Arctic pathways: integrating hydrology, geomorphology and remote sensing in the North

Dissertation (Ph.D.) University of Alaska Fairbanks, 2015This work presents improved approaches for integrating patterns and processes within hydrology, geomorphology, ecology and permafrost on Arctic landscapes. Emphasis was placed on addressing fundamental interdisciplinary questions using robust, repeatable methods. Water tracks were examined in the foothills of the Brooks Range to ascertain their role within the range of features that transport water in Arctic regions. Classes of water tracks were developed using multiple factor analysis based on their geomorphic, soil and vegetation characteristics. These classes were validated to verify that they were repeatable. Water tracks represented a broad spectrum of patterns and processes primarily driven by surficial geology. This research demonstrated a new approach to better understanding regional hydrological patterns. The locations of the water track classes were mapped using a combination method where intermediate processing of spectral classifications, texture and topography were fed into random forests to identify the water track classes. Overall, the water track classes were best visualized where they were the most discrete from the background landscape in terms of both shape and content. Issues with overlapping and imbalances between water track classes were the biggest challenges. Resolving the spatial locations of different water tracks represents a significant step forward for understanding periglacial landscape dynamics. Leaf area index (LAI) calculations using the gap-method were optimized using normalized difference vegetation index (NDVI) as input for both WorldView-2 and Landsat-7 imagery. The study design used groups to separate the effects of surficial drainage networks and the relative magnitude of change in NDVI over time. LAI values were higher for the WorldView-2 data and for each sensor and group combination the distribution of LAI values was unique. This study indicated that there are tradeoffs between increased spatial resolution and the ability to differentiate landscape features versus the increase in variability when using NDVI for LAI calculations. The application of geophysical methods for permafrost characterization in Arctic road design and engineering was explored for a range of conditions including gravel river bars, burned tussock tundra and ice-wedge polygons. Interpretations were based on a combination of Directcurrent resistivity - electrical resistivity tomography (DCR-ERT), cryostratigraphic information via boreholes and geospatial (aerial photographs & digital elevation models) data. The resistivity data indicated the presence/absence of permafrost; location and depth of massive ground ice; and in some conditions changes in ice content. The placement of the boreholes strongly influenced how geophysical data can be interpreted for permafrost conditions and should be carefully considered during data collection strategies

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Earth Resources: A continuing bibliography with indexes (Issue 37)

This bibliography lists 512 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1983. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Drones and Geographical Information Technologies in Agroecology and Organic Farming

Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies. This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license

Utilisation des données d'élévation LiDAR à haute résolution pour la cartographie numérique du matériel parental des sols

Les connaissances sur la morphologie de la Terre sont essentielles à la compréhension d’une variété de processus géomorphologiques et hydrologiques. Des avancées récentes dans le domaine de la télédétection ont significativement fait progresser notre habilité à se représenter la surface de la Terre. Parmi celles-ci, les données d’élévation LiDAR permettent la production de modèles numériques d’altitude (MNA) à haute résolution sur de grands territoires. Le LiDAR est une avancée technologique majeure permettant aux scientifiques de visualiser en détail la morphologie de la Terre et de représenter des reliefs peu prononcés, et ce, même sous la canopée des arbres. Une telle avancée technologique appelle au développement de nouvelles approches innovantes afin d’en réaliser le potentiel scientifique. Dans ce contexte, le présent travail vise à développer deux approches de cartographie numérique utilisant des données d’élévation LiDAR et servant à l’évaluation de la composition du sous-sol. La première approche à être développée utilise la localisation de crêtes de plage identifiées sur des MNA LiDAR afin de modéliser l’étendue maximale de la mer de Champlain, une large paléo-mer régionalement importante. Cette approche nous a permis de cartographier avec précision les 65 000 km2 autrefois inondés par la mer. Ce modèle sert à l’évaluation de la distribution des sédiments marins et littoraux dans les basses-terres du Saint-Laurent. La seconde approche utilise la relation entre des échantillons de matériel parental des sols (MPS) et des attributs topographiques dérivés de données LiDAR afin de cartographier à haute résolution et à une échelle régionale le MPS sur le Bouclier canadien. Pour ce faire, nous utilisons une approche novatrice combinant l’analyse d’image orientée-objet (AIOO) avec une classification par arbre décisionnel. Cette approche nous a permis de produire une carte du MPS à haute résolution sur plus de 185 km2 dans un environnement hétérogène de post-glaciation. Les connaissances issues de la production de ces deux modèles ont permis de conceptualiser la composition du sous-sol dans les régions limitrophes entre les basses-terres du Saint-Laurent et le Bouclier canadien. Ce modèle fournit aux chercheurs et aux gestionnaires de ressources des connaissances détaillées sur la géomorphologie de cette région et contribue à l’amélioration de notre capacité à saisir les services écosystémiques et à prédire les aléas environnementaux liés aux processus du sous-sol.Knowledge of the earth’s morphology is essential to the understanding of many geomorphic and hydrologic processes. Recent advancements in the field of remote sensing have significantly improved our ability to assess the earth’s surface. From these, LiDAR elevation data permits the production of high-resolution digital elevation models (DEMs) over large areas. LiDAR is a major technological advance as it allows geoscientists to visualize the earth’s morphology in high detail, even allowing us to resolve low-relief landforms in forested areas where the surface is obstructed by vegetation cover. Such a technological advance calls for the development of new and novel approaches to realize the scientific potential of this new spatial data. In this context, the present work aims to develop two digital mapping approaches that use LiDAR elevation data for assessing the earth’s subsurface composition. The first approach to be developed uses the location of low-relief beach ridges observed on LiDAR-derived DEMs to map the extent of a large and regionally important paleo-sea, the Champlain Sea. This approach allowed us to accurately map the 65,000 km2 area once inundated by sea water. The model serves to the assessment of the distribution of marine and littoral sediments in the St. Lawrence Lowlands. The second approach uses the relationship between field-acquired samples of soil parent material (SPM) and LiDAR-derived topographic attributes to map SPM at high-resolution and at a regional scale on the Canadian Shield. To do so, we used a novel approach that combined object-based image analysis (OBIA) with a classification tree algorithm. This approach allowed us to produce a fine-resolution 185 km2 map of SPM in a heterogeneous post-glaciation Precambrian Shield setting. The knowledge obtained from producing these two models allowed us to conceptualize the subsurface composition at the limit between the St. Lawrence Lowlands and the Canadian Shield. This insight provides researchers and resource managers with a more detailed understanding of the geomorphology of this area and contributes to improve our capacity to grasp ecosystem services and predict environmental hazards related to subsurface processes