Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.

Extreme wildfires have substantial economic, social and environmental impacts, but there is uncertainty whether such events are inevitable features of the Earth's fire ecology or a legacy of poor management and planning. We identify 478 extreme wildfire events defined as the daily clusters of fire radiative power from MODIS, within a global 10 × 10 km lattice, between 2002 and 2013, which exceeded the 99.997th percentile of over 23 million cases of the ΣFRP 100 km in the MODIS record. These events are globally distributed across all flammable biomes, and are strongly associated with extreme fire weather conditions. Extreme wildfire events reported as being economically or socially disastrous (n = 144) were concentrated in suburban areas in flammable-forested biomes of the western United States and southeastern Australia, noting potential biases in reporting and the absence of globally comprehensive data of fire disasters. Climate change projections suggest an increase in days conducive to extreme wildfire events by 20 to 50% in these disaster-prone landscapes, with sharper increases in the subtropical Southern Hemisphere and European Mediterranean Basin.

Widespread, rapid, drought-, and infestation-triggered tree mortality is emerging as a phenomenon affecting forests globally and may be linked to increasing temperatures and drought frequency and severity. The ecohydrological consequences of forest dieoff have been little studied and remain highly uncertain. To explore this knowledge gap, we apply the extensive literature on the ecohydrological effects of tree harvest in combination with the limited existing die-off ecohydrology research to develop new, relevant hypotheses. Tree mortality results in loss of canopy cover, which directly alters evaporation, transpiration, and canopy interception and indirectly alters other watershed hydrologic processes, including infiltration, runoff, groundwater recharge, and streamflow. Both die-off and harvest research suggest that for most forests, water yield can be expected to increase following substantial loss of tree cover by die-off. We hypothesize that where annual precipitation exceeds ¾500 mm or water yield is dominated by snowmelt, watersheds will experience significantly decreased evapotranspiration and increased flows if absolute canopy cover loss from die-off exceeds 20%. However, recent observations suggest that water yield following die-off can potentially decrease rather than increase in drier forests. To reliably predict die-off responses, more research is needed to test these hypotheses, including observations of multiple water budget components and the persistence of ecohydrological effects with the post-die-off successional dynamics of tree recruitment, understorey growth, and interactions with additional disturbances. With die-off, mitigation and restoration options are limited and costly, necessitating societal adaptation; therefore, die-off ecohydrology should be a high priority for future research. Published in 2011. This article is a US Government work and is in the public domain in the USA.

Stress within the teaching profession has a negative impact on the health and well-being of individual teachers and on retention and recruitment for the profession as a whole. There is increasing literature to suggest that Mindfulness is a useful intervention to address a variety of psychological problems, and that Mindfulness-Based Stress Reduction (MBSR) is a particularly helpful intervention for stress. We investigated the effects of teaching a MBSR course to primary school teachers to reduce stress. The MBSR course was taught to a group of primary school teachers and evaluated to establish its effects on levels of anxiety, depression, and stress, as well as movement towards a stated goal and changes in awareness. The results showed improvement for most participants for anxiety, depression, and stress, some of which were statistically significant. There were also significant improvements on two of the four dimensions of a mindfulness skills inventory. These results suggest that this approach could be a potentially cost-effective method to combat teacher stress and burnout.

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We compared the utility of discrete-return light detection and ranging (lidar) data and multispectral satellite imagery, and their integration, for modeling and mapping basal area and tree density across two diverse coniferous forest landscapes in north-central Idaho. We applied multiple linear regression models subset from a suite of 26 predictor variables derived from discrete-return lidar data (2 m post spacing), advanced land imager (ALI) multispectral (30 m) and panchromatic (10 m) data, or geographic X, Y, and Z location. In general, the lidar-derived variables had greater utility than the ALI variables for predicting the response variables, especially basal area. The variables most useful for predicting basal area were lidar height variables, followed by lidar intensity; those most useful for predicting tree density were lidar canopy cover variables, again followed by lidar intensity. The best integrated models selected via a best-subsets procedure explained-90% of variance in both response variables. Natural-logarithm-transformed response variables were modeled. Predictions were then transformed from the natural logarithm scale back to the natural scale, corrected for transformation bias, and mapped across the two study areas. This study demonstrates that fundamental forest structure attributes can be modeled to acceptable accuracy and mapped with currently available remote sensing technologies. Resume. N ous avons compare l' utilite du lidar 11 retour discret et de l'imagerie satellitaire multispectrale et leur integration pour la modelisation et la cartographie de la surface terriere et la densite des arbres pour deux paysages diversifies de forets de coniferes dans Ie centre-nord de l'Idaho. Nous avons applique les sous-ensembles des modeles de regression lineaire multiple d'une serie de 26 variables predictives derivees de donnees lidar 11 retour discret (post-espacement de 2 m), de donnees multispectrales (30 m) et panchromatiques (10 m) du capteur ALI «< advanced land imager ») ou de localisation geographique en X, Yet Z. En general, les variables derivees du lidar etaient d'une plus grande utilite que les variables ALI pour la prevision des variables dependantes, particulierement la surface terriere. Les variables les plus utiles pour la prevision de la surface terriere des arbres etaient les variables lidar de la hauteur des arbres suivies par l'intensite lidar ; les plus utiles pour la prevision de la densite des arbres etaient les variables lidar du couvert, la aussi suivies par l'intensite lidar. Les meilleurs modeles integres selectionnes via une procedure du meilleur sous-ensemble a permis d' expliquer-90% de la variance pour les deux vaiables dependantes. Les variables dependantes transformees par logarithme naturel ont ete modelisees. Les previsions ont alors ete transformees de l'echelle In, puis 11 l'echelle naturelle, corrigees pour Ie biais lie 11 la transformation et cartographiees sur l'ensemble des deux regions d'etude. Cette etude demontre que les attributs fondamentaux de la struct...

We describe and evaluate a new analysis technique, spatial wavelet analysis (SWA), to automatically estimate the location, height, and crown diameter of individual trees within mixed conifer open canopy stands from light detection and ranging (lidar) data. Two-dimensional Mexican hat wavelets, over a range of likely tree crown diameters, were convolved with lidar canopy height models. Identification of local maxima within the resultant wavelet transformation image then allowed determination of the location, height, and crown diameters of individual trees. In this analysis, which focused solely on individual trees within open canopy forests, 30 trees incorporating seven dominant North American tree species were assessed. Two-dimensional (2D) wavelet-derived estimates were well correlated with field measures of tree height (r = 0.97) and crown diameter (r = 0.86). The 2D wavelet-derived estimates compared favorably with estimates derived using an established method that uses variable window filters (VWF) to estimate the same variables but relies on a priori knowledge of the tree height-crown diameter relationship. The 2D spatial wavelet analysis presented herein could potentially allow automated, large-scale, remote estimation of timber board feet, foliar biomass, canopy volume, and aboveground carbon, although further research testing the limitations of the method in a variety of forest types with increasing canopy closures is warranted. Résumé. Nous décrivons et évaluons une nouvelle technique d'analyse, l'analyse spatiale en ondelettes (ASO) pour estimer automatiquement la localisation, la hauteur et le diamètre de la couronne des arbres individuels dans des peuplements de conifères mixtes à couvert ouvert à partir de données lidar (light detection and ranging). Des ondelettes à deux dimensions de type chapeau mexicain pour une variété de diamètres de couronnes d'arbres potentiels ont été convoluées avec des modèles de hauteur de couvert lidar. L'identification de maximums locaux à l'intérieur de l'image de transformation en ondelettes résultante a permis par la suite la détermination de la localisation, de la hauteur et des diamètres des couronnes des arbres individuels. Dans cette analyse, qui s'est concentrée seulement sur les arbres individuels à l'intérieur de forêts à couvert ouvert, 30 arbres représentant sept espèces dominantes d'arbres d'Amérique du Nord ont été évalués. Les estimations 2-D dérivées des ondelettes étaient bien corrélées avec les mesures de terrain de la hauteur des arbres (r = 0,97) et du diamètre de la couronne (r = 0,86). Les estimations 2-D dérivées des ondelettes se comparaient avantageusement aux estimations dérivées à l'aide d'une méthode établie utilisant des filtres de fenêtre variable (FFV) pour l'estimation de ces mêmes variables, mais qui est basée sur la connaissance a priori de la relation hauteur des arbres/diamètre de la couronne. L'analyse spatiale en ondelettes 2-D présentée ici pourrait potentiellement permettre l'estimation automatisée à grande échelle par télédétecti...