The U.S. Arctic Observing Viewer: A Web-Mapping Application for Enhancing Environmental Observation of the Changing Arctic

Although much progress has been made with various Arctic Observing efforts, assessing that progress can be difficult. What data collection efforts are established or underway? Where? By whom? To help meet the strategic needs of programs such as the U.S. Study of Environmental Arctic Change (SEARCH), the Arctic Observing Network (AON), Sustaining Arctic Observing Networks (SAON) and related initiatives, an update has been released for the Arctic Observing Viewer (AOV; http://ArcticObservingViewer.org). This web mapping application and information system has begun to compile the who, what, where, and when for thousands of data collection sites (such as boreholes, ship tracks, buoys, towers, sampling stations, sensor networks, vegetation sites, stream gauges, and observatories) wherever marine, terrestrial, or atmospheric data are collected. Contributing partners for this collaborative resource include the U.S. NSF, ACADIS, ADIwg, AOOS, a2dc, AON, ARMAP, BAID, CAFF, IASOA, INTERACT, and others. While focusing on U.S. activities, the AOV welcomes information exchange with international groups for mutual benefit. Users can visualize, navigate, select, search, draw, print, and more. AOV is founded on principles of interoperability, with open metadata and web service standards, so that agencies and organizations can use AOV tools and services for their own purposes. In this way, AOV will reinforce and complement other distributed yet interoperable cyber-resources and will help science planners, funding agencies, researchers, data specialists, and others to assess status, identify overlap, fill gaps, optimize sampling design, refine network performance, clarify directions, access data, coordinate logistics, collaborate, and more in order to meet Arctic Observing goals.Malgré les progrès réalisés dans le cadre de nombreux efforts d’observation de l’Arctique, les progrès peuvent être difficiles à évaluer. Quelles initiatives de collecte de données sont en cours ou sont établies? À quel endroit? Et qui gère ces initiatives? Pour aider à répondre aux besoins stratégiques de programmes comme ceux de l’organisme américain Study of Environmental Arctic Change (SEARCH), du réseau Arctic Observing Network (AON), des réseaux Sustaining Arctic Observing Networks (SAON) et d’autres programmes connexes, on a procédé à la mise à jour de l’Arctic Observing Viewer (AOV; http://ArcticObservingViewer.org). Ce système d’information jumelé à une application de mappage sur le Web a amorcé la compilation des coordonnées et des renseignements se rapportant à des milliers de sites de collecte de données (comme les trous de forage, les trajets de navires, les bouées, les tours, les stations d’échantillonnage, les réseaux de capteurs, les sites de végétation, les fluviomètres et les observatoires) où des données marines, terrestres ou atmosphériques sont prélevées. Parmi les partenaires qui collaborent à cette ressource, notons U.S. NSF, ACADIS, ADIwg, AOOS, a2dc, AON, ARMAP, BAID, CAFF, IASOA, INTERACT et d’autres encore. Bien que l’AOV se concentre sur les activités américaines, il accepte l’échange d’information avec des groupes internationaux lorsqu’il existe des avantages mutuels. Les utilisateurs peuvent visualiser les données, naviguer dans le système, faire des sélections et des recherches, dessiner, imprimer et ainsi de suite. L’AOV fonctionne moyennant des principes d’interopérabilité, avec des métadonnées ouvertes et des normes de service sur le Web afin que les organismes et les organisations puissent utiliser les outils et les services de l’AOV pour leurs propres fins. De cette façon, l’AOV sera en mesure de consolider et de compléter d’autres cyberressources à la fois réparties et interopérables, en plus d’aider les planificateurs de la science, les bailleurs de fonds, les chercheurs, les spécialistes des données et d’autres encore à évaluer les statuts, à repérer les dédoublements, à combler les écarts, à optimiser les plans d’échantillonnage, à raffiner le rendement des réseaux, à clarifier les consignes, à accéder aux données, à coordonner la logistique, à collaborer et ainsi de suite afin de répondre aux objectifs d’observation de l’Arctique

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non‐forest ecosystems

P. 1-15Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1–10 ha−1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.S

Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches

Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method)