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Tree mortality has escalated worldwide in recent years due to climate warming and unprecedented drought events. However, mapping tree mortality across forest ecosystems has not yet been achieved. Aerial photos provide opportunities to reveal the spatial and spectral characteristics of canopy death at local to landscape scales. In this work, we present a deep learning model for mapping tree mortality from aerial photos in various forested ecosystems across Europe. This model builds on a baseline model trained with data on dead tree canopies from California using sub-meter resolution aerial photos and allows the use of various spatial resolutions of the input images (ranging from 10 to 60 cm). By comparing our results to ground observations and/or state-of-the-art forest disturbance and loss products, we will discuss the advantages and limitations of aerial photo-based tree mortality mapping. The proposed framework can be used for large-scale mapping of tree mortality from multi-year aerial photos. The tree mortality maps provide detailed information that can help understand the mechanisms of tree mortality under climate change. Furthermore, aerial photo-based maps can serve as training labels for mapping pixel-level deadwood fractions from satellite images, which enables seamless spatial coverage and could be an essential step towards a global map of tree mortality.

Excess tree mortality in the wake of climate extremes has been observed globally. However, we still lack precise data on mortality at global scale to understand respective drivers and spatiotemporal dynamics. The Sentinel-2 satellite fleet, equipped with the MultiSpectral Instrument (MSI), covers the entire earth on average every five days at spatial resolutions ranging from 10 m to 60 m. Mapping tree mortality from Sentinel-2 globally in diverse ecosystems requires equally diverse reference data. Using globally distributed high-resolution aerial orthoimagery reference data and artificial intelligence methods, we can translate spectral signatures of remote sensing into deadwood. Specifically, in this study we show how to predict the share of standing deadwood for a 10 m pixel in a specific year. The method takes into account temporal patterns, spatial context, as well as all Sentinel-2 spectral bands. This will enable us to map tree mortality globally at a new level of precision.

We have observed tree die-offs in a variety of regions in the world. Understanding the diverse causes of tree mortality requires exact information about which trees are dying and where. With the increased user-friendliness of drones and the availability of airborne imagery, high-resolution imagery of forests is becoming widely available. Delineating standing deadwood in such aerial imagery has become a classic segmentation task and several models with varying accuracy have been developed. However, these machine-learning based models are not generic and limited to specific image resolutions, sensor characteristics, geographic regions, and forest ecosystems. The reason for this lack of generality is that previous models have been trained using only datasets representative of specific regions and obtained from a single source. In this study, we obtain a diverse dataset spanning more than a dozen countries across continents and implement a single convolutional neural network (CNN) model that is able to cope with most forest ecosystems, varying image quality, and spatial resolutions.

Excessive tree mortality rates prevail in many regions of the world. Understanding tree mortality dynamics remains elusive as this multifaceted phenomenon is influenced by an interplay of abiotic and biotic factors including, but not limited to, global warming, climate extremes, pests, pathogens, and other environmental stressors. Earth observation satellites, coupled with machine learning, present a promising avenue to unravel map standing dead trees and lay the foundation for explaining the underlying dynamics. However, the lack of globally comprehensive, georeferenced training data spanning various biomes and forest types has hindered the development of a unified global product detailing tree mortality patterns. Present ground-based observations, e.g., sourced from national inventories, are often sparse, lack standardization, and spatial specificity. Alternatively, aerial imagery captured via drones or airplanes in concert with computer vision methods offers a potent resource for mapping standing deadwood with high precision and efficiency on local scales. Such products can subsequently be used to train models based on satellite data to infer standing deadwood on large spatial scales. In pursuit of harnessing this potential to enhance our global comprehension of tree mortality patterns, we initiated the development of a dynamic database (https://deadtrees.earth), which enables to 1) upload and download aerial imagery with optional labels on standing deadwood, 2) automatically detect (semantic segmentation) standing dead trees in uploaded aerial imagery through a generic detection computer vision model, 3) Visualization and download of extensive spatiotemporal tree mortality products derived from extrapolating standing deadwood using Earth observation data. This presentation provides an in-depth overview of the deadtrees.earth database, outlining its motivation, current status, and future perspectives. By integrating Earth observation, m

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, not standardized and not spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map tree mortality over time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Drones provide a cost-effective source of training data by capturing high-resolution orthophotos of tree mortality events at sub-centimeter resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than a thousand centimeter-resolution orthophotos, covering already more than 300,000 ha, of which more than 58,000 ha are fully annotated. This community-sourced and rigorously curated dataset shall serve as a foundation for a global initiative to gather comprehensive reference data. In concert with Earth observation data and machine learning it will serve to uncover tree mortality patterns from local to global scales. This will provide the foundation to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. Thus, the open and interactive nature of deadtrees.earth together with the collective effort of the community is meant to continuously increase our capacity to uncover and understand tree mortality patterns.Competing Interest StatementThe authors have declared no competing interest.

Pinpointing autonomous systems which deploy specific inter-domain techniques such as Route Flap Damping (RFD) or Route Origin Validation (ROV) remains a challenge today. Previous approaches to detect per-AS behavior often relied on heuristics derived from passive and active measurements. Those heuristics, however, often lacked accuracy or imposed tight restrictions on the measurement methods. We introduce an algorithmic framework for network tomography, BeCAUSe, which implements Bayesian Computation for Autonomous Systems. Using our original combination of active probing and stochastic simulation, we present the first study to expose the deployment of RFD. In contrast to the expectation of the Internet community, we find that at least 9% of measured ASs enable RFD, most using deprecated vendor default configuration parameters. To illustrate the power of computational Bayesian methods we compare BeCAUSe with three RFD heuristics. Thereafter we successfully apply a generalization of the Bayesian method to a second challenge, measuring deployment of ROV.