Drought has promoted large-scale, insect-induced tree mortality in recent years, with severe consequences for ecosystem function, atmospheric processes, sustainable resources and global ...biogeochemical cycles. However, the physiological linkages among drought, tree defences, and insect outbreaks are still uncertain, hindering our ability to accurately predict tree mortality under ongoing climate change. Here we propose an interdisciplinary research agenda for addressing these crucial knowledge gaps. Our framework includes field manipulations, laboratory experiments, and modelling of insect and vegetation dynamics, and focuses on how drought affects interactions between conifer trees and bark beetles. We build upon existing theory and examine several key assumptions: (1) there is a trade-off in tree carbon investment between primary and secondary metabolites (e.g. growth vs defence); (2) secondary metabolites are one of the main component of tree defence against bark beetles and associated microbes; and (3) implementing conifer-bark beetle interactions in current models improves predictions of forest disturbance in a changing climate. Our framework provides guidance for addressing a major shortcoming in current implementations of large-scale vegetation models, the under-representation of insect-induced tree mortality.
Remote sensing is a well-established tool for detecting forest disturbances. The increased availability of uncrewed aerial systems (drones) and advances in computer algorithms have prompted numerous ...studies of forest insects using drones. To date, most studies have used height information from three-dimensional (3D) point clouds to segment individual trees and two-dimensional multispectral images to identify tree damage. Here, we describe a novel approach to classifying the multispectral reflectances assigned to the 3D point cloud into damaged and healthy classes, retaining the height information for the assessment of the vertical distribution of damage within a tree. Drone images were acquired in a 27-ha study area in the Northern Rocky Mountains that experienced recent damage from insects and then processed to produce a point cloud. Using the multispectral data assigned to the points on the point cloud (based on depth maps from individual multispectral images), a random forest (RF) classification model was developed, which had an overall accuracy (OA) of 98.6%, and when applied across the study area, it classified 77.0% of the points with probabilities greater than 75.0%. Based on the classified points and segmented trees, we developed and evaluated algorithms to separate healthy from damaged trees. For damaged trees, we identified the damage severity of each tree based on the percentages of red and gray points and identified top-kill based on the length of continuous damage from the treetop. Healthy and damaged trees were separated with a high accuracy (OA: 93.5%). The remaining damaged trees were separated into different damage severities with moderate accuracy (OA: 70.1%), consistent with the accuracies reported in similar studies. A subsequent algorithm identified top-kill on damaged trees with a high accuracy (OA: 91.8%). The damage severity algorithm classified most trees in the study area as healthy (78.3%), and most of the damaged trees in the study area exhibited some amount of top-kill (78.9%). Aggregating tree-level damage metrics to 30 m grid cells revealed several hot spots of damage and severe top-kill across the study area, illustrating the potential of this methodology to integrate with data products from space-based remote sensing platforms such as Landsat. Our results demonstrate the utility of drone-collected data for monitoring the vertical structure of tree damage from forest insects and diseases.
Fire Refugia MEDDENS, ARJAN J.H.; KOLDEN, CRYSTAL A.; LUTZ, JAMES A. ...
Bioscience,
12/2018, Letnik:
68, Številka:
12
Journal Article
Recenzirano
Odprti dostop
Fire refugia are landscape elements that remain unburned or minimally affected by fire, thereby supporting postfire ecosystem function, biodiversity, and resilience to disturbances. Although fire ...refugia have been studied across continents, scales, and affected taxa, they have not been characterized systematically over space and time, which is crucial for understanding their role in facilitating resilience in the context of global change. We identify four dichotomies that delineate an overarching conceptual framework of fire refugia: unburned versus lower severity, species-specific versus landscape-process characteristics, predictable versus stochastic, and ephemeral versus persistent. We outline the principal concepts underlying the ecological function of fire refugia and describe both the role of fire refugia and uncertainties regarding their persistence under global change. An improved understanding of fire refugia is crucial to conservation given the role that humans play in shaping disturbance regimes across landscapes.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•Landsat imagery was used to predict percent beetle-caused tree mortality.•Cumulative tree mortality was 22% of the study area, equivalent to 228million trees.•Mortality patterns showed expansion ...early and intensification later in the outbreak.•Plot-scale outbreak areas (30-m grid cells) experienced 60% mortality on average.•Beetles moved through plot-scale areas on average in 3–4years and killed 20%/year.
Mountain pine beetles (Dendroctonus ponderosae Hopkins) have killed billions of trees in the United States and Canada in the last decades, thereby causing major alterations to forest ecosystems. Therefore, monitoring the extent and patterns of these major disturbance events are important for subsequent forest management. At 30-m spatial resolution, Landsat imagery affords the opportunity for quantifying spatial and temporal patterns of bark beetle outbreaks at plot to stand resolution. We developed a continuous measure of bark beetle-caused tree mortality using multi-temporal Landsat data (1996–2011) in the Rocky Mountains of northcentral Colorado. We report the year of detection of tree mortality, which is 1year after attack by beetles. Two approaches were used to predict percent red stage tree mortality within a 30-m grid cell, multiple linear regression models and generalized additive models (GAM) with a nonlinear spatial term. Both models explained >75% of the variance using three Landsat spectral explanatory variables. We used the linear model to predict tree mortality across the entire study area and time series because of its simplicity, its capability for extrapolation beyond the training area, and similar performance compared with the GAM. From 1996 to 2011, cumulative tree mortality was 22% of forested areas within the Landsat scene, equivalent to 228million mature lodgepole pine trees (range: 174–332million trees using the 95% confidence interval of field-measured crown areas). Early in the outbreak, tree mortality was associated with expansion, whereas later in the outbreak, mortality was due to intensification (increase in mortality within areas already having beetle activity). We used three metrics (cumulative tree mortality, duration of tree mortality, and average rate of tree mortality) to investigate the temporal and spatial patterns of bark beetle-caused mortality using different grid cell resolutions within the Landsat scene. At the Landsat spatial resolution (900m2), we estimated that grid cells within the outbreak experienced means of 60% mortality, a duration of 3–4years, and an average rate of mortality of 20%/year. With coarser spatial resolution, cumulative tree morality and rate of tree mortality decreased whereas duration of tree mortality increased. Our results improve the understanding of spatial and temporal patterns of tree mortality caused by bark beetle outbreaks, and provide specific information for forest managers and scientists about a severe mountain pine beetle infestation in northcentral Colorado.
•There is a lack of a comprehensive monitoring system of current terrestrial disturbances.•Remote sensing offers enormous potential for global disturbance observation.•An idealized monitoring system ...should accurately detect disturbances and identify their cause(s)•An ecological perspective of remote sensing is vital to continue development of terrestrial disturbance monitoring.
Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide.
Disturbance refugia – locations that experience less severe or frequent disturbances than the surrounding landscape – provide a framework to highlight not only where and why these biological legacies ...persist as adjacent areas change but also the value of those legacies in sustaining biodiversity. Recent studies of disturbance refugia in forest ecosystems have focused primarily on fire, with a growing recognition of important applications to land management. Given the wide range of disturbance processes in forests, developing a broader understanding of disturbance refugia is important for scientists and land managers, particularly in the context of anthropogenic climate change. We illustrate the framework of disturbance refugia through the individual and interactive effects of three prominent forest disturbance agents: fire, drought, and insect outbreaks. We provide examples of disturbance refugia and related applications to natural resource management in western North America, demonstrate methods for characterizing refugia, identify research priorities, and discuss why a more comprehensive definition of disturbance refugia is relevant to conservation globally.
A changing climate is altering ecosystem carbon dynamics with consequences for natural systems and human economies, but there are few tools available for land managers to meaningfully incorporate ...carbon trajectories into planning efforts. To address uncertainties wrought by rapidly changing conditions, many practitioners adopt resistance and resilience as ecosystem management goals, but these concepts have proven difficult to monitor across landscapes. Here, we address the growing need to understand and plan for ecosystem carbon with concepts of resistance and resilience. Using time series of carbon fixation (n = 103), we evaluate forest management treatments and their relative impacts on resistance and resilience in the context of an expansive and severe natural disturbance. Using subalpine spruce–fir forest with a known management history as a study system, we match metrics of ecosystem productivity (net primary production, g C m−2 year−1) with site‐level forest structural measurements to evaluate (1) whether past management efforts impacted forest resistance and resilience during a spruce beetle (Dendroctonus rufipennis) outbreak, and (2) how forest structure and physiography contribute to anomalies in carbon trajectories. Our analyses have several important implications. First, we show that the framework we applied was robust for detecting forest treatment impacts on carbon trajectories, closely tracked changes in site‐level biomass, and was supported by multiple evaluation methods converging on similar management effects on resistance and resilience. Second, we found that stand species composition, site productivity, and elevation predicted resistance, but resilience was only related to elevation and aspect. Our analyses demonstrate application of a practical approach for comparing forest treatments and isolating specific site and physiographic factors associated with resistance and resilience to biotic disturbance in a forest system, which can be used by managers to monitor and plan for both outcomes. More broadly, the approach we take here can be applied to many scenarios, which can facilitate integrated management and monitoring efforts.
Relatively little is known of how the world's largest vegetation transition zone – the Forest Tundra Ecotone (FTE) – is responding to climate change. Newly available, satellite-derived time-series of ...the photochemical reflectance index (PRI) across North America and Europe could provide new insights into the physiological response of evergreen trees to climate change by tracking changes in foliar pigment pools that have been linked to photosynthetic phenology. However, before implementing these data for such purpose at these evergreen dominated systems, it is important to increase our understanding of the fine scale mechanisms driving the connection between PRI and environmental conditions. The goal of this study is thus to gain a more mechanistic understanding of which environmental factors drive changes in PRI during late-season phenological transitions at the FTE – including factors that are susceptible to climate change (i.e., air- and soil-temperatures), and those that are not (photoperiod). We hypothesized that late-season phenological changes in foliar pigment pools captured by PRI are largely driven by photoperiod as opposed to less predictable drivers such as air temperature, complicating the utility of PRI time-series for understanding climate change effects on the FTE. Ground-based, time-series of PRI were acquired from individual trees in combination with meteorological variables and photoperiod information at six FTE sites in Alaska. A linear mixed-effects modeling approach was used to determine the significance (α = 0.001) and effect size (i.e., standardized slope b*) of environmental factors on late-seasonal changes in the PRI signal. Our results indicate that photoperiod had the strongest, significant effect on late-season changes in PRI (b* = 0.08, p < 0.001), but environmental variables susceptible to climate change were also significant (i.e., daily mean solar radiation (b* = −0.03, p < 0.001) and daily mean soil temperature (b* = 0.02, p < 0.001)). These results suggest that interpreting PRI time-series of late-season phenological transitions may indeed facilitate our understanding of how northern treeline responds to climate change.
•Little is known how the world's largest ecotone, the FTE, response to climate change.•PRI time-series could provide novel insights.•We tested if late-season changes of PRI respond to climate or photoperiod.•Changes in late-season PRI respond to photoperiod but also climate.•Results highlight potential of PRI time-series to monitor climate effects on the FTE.
In contrast to abrupt changes caused by land cover conversion, subtle changes driven by a shift in the condition, structure, or other biological attributes of land often lead to minimal and slower ...alterations of the terrestrial surface. Accurate mapping and monitoring of subtle change are crucial for an early warning of long-term gradual change that may eventually result in land cover conversion. Freely accessible moderate-resolution datasets such as the Landsat archive have great potential to characterize subtle change by capturing low-magnitude spectral changes in long-term observations. However, past studies have reported limited success in accurately extracting subtle changes from satellite-based time series analysis. In this study, we introduce a supervised framework named ‘PIDS’ to detect subtle forest disturbance from a comprehensive Landsat data archive by leveraging disturbance-based calibration sites. PIDS consists of four components: (1) Parameter optimization; (2) Index selection; (3) Dynamic stratified monitoring; and (4) Spatial consideration. PIDS was applied to map the early stage of bark beetle infestations (i.e., a lower per-pixel fraction of trees cover that show visual signs of infestation), which are a typical example of subtle change in conifer forests. Landsat Analysis Ready Data were used as the time series inputs for mapping mountain pine beetle and spruce beetle disturbance between 2001 and 2019 in Colorado, USA. PIDS-detection map assessment showed that the overall performance of PIDS (namely ‘F1 score’) was 0.86 for mountain pine beetle and 0.73 for spruce beetle, making a substantial improvement (> 0.3) compared to other approaches/products including COntinuous monitoring of Land Disturbance, LandTrendr, and the National Land Cover Database forest disturbance product. A sub-pixel analysis of tree canopy mortality percentage was performed by linking classified high-resolution (0.3- and 1-m) aerial imagery and 30-m PIDS-detection maps. Results show that PIDS typically detects mountain pine beetle infestation when ≥56% of a Landsat pixel is occupied by red-stage canopy mortality (one year after initial infestation), and spruce beetle infestation when ≥55% is occupied by gray-stage mortality (two years after initial infestation). This study addresses an important methodological goal pertinent to the utility of event-based reference samples for detecting subtle forest change, which could be potentially applied to other types of subtle land change.
•Develop a framework for detecting subtle changes from satellite time series.•Automatically calibrate model parameters and optimize spectral indices.•Assess results using multiple sources including field data and aerial survey.•Reach >0.3 F1 score improvement compared to other approaches.•Subpixel analysis for detected beetle stage using high-resolution imagery.
Bark beetles cause significant tree mortality in coniferous forests across North America. Mapping beetle-caused tree mortality is therefore important for gauging impacts to forest ecosystems and ...assessing trends. Remote sensing offers the potential for accurate, repeatable estimates of tree mortality in outbreak areas. With the advancement of multi-temporal disturbance detection methods using Landsat data, the capability exists for improvement in mapping methods, yet more information is needed to determine the accuracy of these methods for mapping forest disturbances and to quantify differences between these methods and single-date image classification methods. We compared single-date (using maximum likelihood classification) to multi-date (using time series of spectral indices) classification methods of Landsat imagery and investigated how detection accuracy changed with varying levels of mortality severity. For each method, we evaluated several bands and/or spectral vegetation indices and identified the one that resulted in the highest accuracy. A fine-resolution classified aerial image within the Landsat scene was used as reference data for evaluation and comparison between methods. For the single-date image classification, we achieved a 91.0% (kappa=0.88) overall accuracy with 11.7% omission and 2.3% commission errors for the red stage (tree mortality) class using the tasseled cap transformation indices of brightness, greenness, and wetness. For the multi-date analysis, the Band5/Band4 anomaly produced the highest accuracy among spectral indices and resulted in a 89.6% (kappa=0.86) classification accuracy with 12.6% omission and 7.1% commission errors for the red stage class. We compared accuracies between the best single- and multi-date methods across a range of tree mortality within a pixel. The multi-date method was more accurate at intermediate levels of tree mortality, whereas the single-date method was more accurate at high mortality levels. Our results indicate that Landsat-based mapping of forest disturbances that use either single-date or multi-date methods can result in high classification accuracy.
► We evaluated methods to detect tree mortality using Landsat imagery. ► We compared classifications using single- and multi-date imagery. ► We evaluated multiple band/spectral index combinations. ► Methods using single- and multi-date imagery yielded similar accuracies. ► Classification accuracy increased with higher percent tree mortality.
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