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Disturbances such as fire and harvesting shape forest dynamics and must be accounted for when modelling forest properties. However, acquiring timely disturbance information for all of Canada's large forest area has always been challenging. Therefore, we developed an approach to detect annual forest change resulting from fire, harvesting, or flooding using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery at 250 m spatial resolution across Canada and to estimate the within-pixel fractional change (FC). When this approach was applied to the period from 2000 to 2011, the accuracy of detection of burnt, harvested, or flooded areas against our validation dataset was 82%, 80%, and 85%, respectively. With FC, 77% of the area burnt and 82% of the area harvested within the validation dataset were correctly identified. The methodology was optimized to reduce the commission error but tended to omit smaller disturbances as a result. For example, the omitted area for harvest blocks greater than 80 ha was less than 14% but increased to between 38% and 50% for harvest blocks of 20 to 30 ha. Detection of burnt and harvested areas in some regions was hindered by persistent haze or cloud cover or by insect outbreaks. All resulting data layers are available as supplementary material. Key words: boreal forest, National Burned Area Composite, remote sensing, regression tree, decision tree, change detection. Les perturbations telles que le feu et la coupe faconnent la dynamique forestiere et doivent etre prises en compte lorsqu'on modelise les proprietes de la foret. Cependant, l'acquisition rapide de l'information au sujet des perturbations pour l'ensemble des vastes superficies de foret du Canada a toujours presente un defi. Nous avons par consequent developpe une approche pour detecter les changements annuels de la foret dus au feu, a la coupe ou aux inondations en utilisant l'imagerie MODIS avec une resolution spatiale de 250 m partout au Canada et pour estimer le changement fractionnel (FC) a l'echelle du pixel. Lorsque appliquee a la periode allant de 2000 a 2011, la precision de la detection des superficies brulees, coupees ou inondees sur la base de notre jeu de donnees de validation atteignait respectivement 82,80 et 85%. Avec le FC, 77% des superficies brulees et 82% des superficies coupees ont ete correctement identifiees dans le jeu de donnees de validation. La methodologie a ete optimisee pour reduire les erreurs dues a la detection de faux changements avec le resultat cependant qu'elle a tendance a ne pas detecter les plus petites perturbations. Par exemple, les superficies non detectees pour les aires de coupe plus grandes que 80 ha etaient inferieures a 14%, mais atteignaient entre 38 et 50% pour les aires de coupe de 20 a 30 ha. La detection des superficies brulees et coupees etait difficile dans certaines regions du a la brume ou au couvert nuageux persistant, ou par les epidemies d'insecte. Toutes les couches de donnees resultantes sont disponibles dans la section Materiel supplementaire. [Traduit par la Redaction] Mots-cles: foret boreale, Composite nationale des superficies brulees, teledetection, arbre de regression, arbre de decision, detection de changement., Introduction The managed forests of Canada cover approximately 229 Mha (Environment Canada 2012). Much of this area is in the boreal forest, which is characterized by slow growth and significant [...]

How do we gauge understanding? Tests of understanding, such as Turing's imitation game, are numerous; yet, attempts to achieve a state of understanding are not satisfactory assessments. Intelligent agents designed to pass one test of understanding often fall short of others. Rather than approaching understanding as a system state, in this paper, we argue that understanding is a process that changes over time and experience. The only window into the process is through the lens of natural language. Usefully, failures of understanding reveal breakdowns in the process. We propose a set of natural language-based probes that can be used to map the degree of understanding a human or intelligent system has achieved through combinations of successes and failures., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Blaha, Abrams, Bibyk, Bonial, Hartzler, Hsu, Khemlani, King, St. Amant, Trafton and Wong.)

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.