We describe a polar coordinate transformation of vegetation index profiles which permits a broad-scale comparison of location-specific phenological variability influenced by climate, topography, land use, and other factors. We apply statistical data reduction techniques to identify fundamental dimensions of phenological variability and to classify phenological types with intuitive ecological interpretation. Remote sensing-based land surface phenology can reveal ecologically meaningful vegetational diversity and dynamics across broad landscapes. Land surface phenology is inherently complex at regional to continental scales, varying with latitude, elevation, and multiple biophysical factors. Quantifying phenological change across ecological gradients at these scales is a potentially powerful way to monitor ecological development, disturbance, and diversity. Polar coordinate transformation was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series spanning 2000-2018 across North America. In a first step, 46 NDVI values per year were reduced to 11 intuitive annual metrics, such as the midpoint of the growing season and degree of seasonality, measured relative to location-specific annual phenological cycles. Second, factor analysis further reduced these metrics to fundamental phenology dimensions corresponding to annual timing, productivity, and seasonality. The factor analysis explained over 95% of the variability in the metrics and represented a more than ten-fold reduction in data volume from the original time series. In a final step, phenological classes (‘phenoclasses’) based on the statistical clustering of the factor data, were computed to describe the phenological state of each pixel during each year, which facilitated the tracking of year-to-year dynamics. Collectively the phenology metrics, factors, and phenoclasses provide a system for characterizing land surface phenology and for monitoring phenological change that is indicative of ecological gradients, development, disturbance, and other aspects of landscape-scale diversity and dynamics.

Regional ecological assessments evaluate sustainability as an interaction among ecosystem services and stressors across changing landscapes. Using ecological assessments to inform ecosystem management activities relies on assessing functional linkages between ecosystem processes and ecosystem services, because ecosystem processes are the primary targets of ecosystem management. We undertook a review of regional ecological assessments in the Appalachian region of the United States to examine how forest-based ecosystem services, forest ecosystem processes, and their linkages are quantified. To provide context, we first give an overview of common ecological assessment frameworks, including risk, vulnerability, resilience, and indicator-based approaches. Assessments tended to target either ecosystem-level properties thought to be important for ecosystem service sustainability, or else to target specific ecosystem services or stressors. Forest ecosystem-level assessment most often relied on specific indicators for system properties such as integrity or health, but how those properties or their indicators were related to ecosystem services was typically not quantified. Individual ecosystem services were frequently assessed in terms of risk and vulnerability to specific external stressors, but linkages to ecosystem processes, and potential tradeoffs among ecosystem services, were infrequently quantified. Integrated system-level assessment and ecosystem service assessment can improve support for ecosystem management by advancing our understanding of dependencies on the ecosystem processes that are modified through management. Models that evaluate ecosystem services and underlying processes in a systems context offer one approach to do so.

Wildfire occurrences have increased and are projected to continue increasing globally. Strategic, evidence-based planning with diverse stakeholders, making use of diverse ecological and social data, is crucial for confronting and mitigating the associated risks. Prescribed fire, when planned and executed carefully, is a key management tool in this effort. Assessing where prescribed fire can be a particularly effective forest management tool can help prioritize efforts, reduce wildfire risk, and support fire-resilient lands and communities. We collaborated with expert stakeholders to develop a Bayesian network model that integrated a large variety of biophysical, socioecological, and socioeconomic spatial information for the Southeastern United States to quantify where risk is high and where prescribed fire would be efficient in mitigating risk. The model first estimated wildfire risk based on landscape-scale interactions among the likelihoods of fire occurrence and severity and the people and resources potentially exposed—accounting for socioeconomic vulnerabilities as well as key ecosystem services. The model then quantified the potential for risk reduction through prescribed fire, given the existing fuel load, climate, and other landscape conditions. The resulting expected risk estimates show high risk concentrated in the coastal plain and interior highland subregions of the Southern US, but there was considerable variation among risks to different ecosystem services and populations, including potential exposure to smoke emissions. The capacity to reduce risk through fuel reductions was spatially correlated with risk; where these diverged, the difference was largely explained by fuel load. We suggest that both risk and the capacity for risk reduction are important in identifying priorities for management interventions. The model serves as a decision support tool for stakeholders to coordinate large-landscape adaptive management initiatives in the Southern US. The model is flexible with regard to both empirical and expert-driven parameterizations and can be updated as new knowledge and data emerge. The resulting spatial information can help connect active management options to forest management goals and make management more efficient through targeted investments in priority landscapes.

Risks associated with severe wildfire are growing in forest landscapes due to interactions among climate change, fuel accumulation from fire suppression, an expanding wildland–urban interface, and additional factors. People, infrastructure, ecosystem services, and forest health all face varying degrees of risk. The spatial distributions of the many social and ecological factors that influence wildfire, its impacts, and management responses are an important landscape-level context for managing risks and fostering resilient lands and communities. Decision-support tools that integrate these varied distributions can provide a holistic and readily interpreted characterization of landscapes, helping fire management decision making be appropriate, efficient, and effective. Firescapes—landscape types defined in relation to fire, its drivers, and its effects as a socioecological system—fill this role, providing a way to organize and interpret spatial variation along multiple relevant dimensions. We describe a quantitative approach for classifying and mapping firescapes for decision support, using the southeastern United States as a case study. We worked with regional partners to compile relevant large-scale datasets and identify 73 variables for analysis. We used factor analysis to reduce the data to eight factors with intuitive interpretations relevant to fire dynamics, fire history, forest characteristics, climate, conservation and ecosystem service values, social and ecological landscape properties, and social vulnerabilities. We then used cluster analysis on the factors to generate quantitative landscape classes, which we interpreted as nine distinctive firescape classes. The firescapes provide a broad-scale socioecological information context for wildfire risk management and planning. The analytical approach can accommodate different data types at a variety of scales, incorporate new monitoring data as they are available, and can be used under data-driven scenarios to assess possible consequences of future change. The resulting firescape maps can provide decision support to forest managers, planners, and other stakeholders, informing appropriate strategies to manage fire and associated risks, build community and forest resilience to fire, and improve conservation outcomes.

Context: Characterizing landscape ecological complexity and change requires integrated description of spatial and temporal landscape organization and dynamics, as suggested by the shifting mosaic concept. Remotely sensed land surface phenology allows the detection of even small differences among landscape patches and through time, allowing for the analysis of landscapes as shifting mosaics. Objectives: We sought to quantify aspects of the complex landscape behaviors that are implied by spatiotemporal variation in land surface phenology. We adapted an information-theoretic (IT) framework from ecosystem ecology to capture landscape-level spatiotemporal complexity and organization and map these properties across large areas. Methods: Phenology data were derived from remotely sensed, pixel-level time series of a vegetation greenness index, across a large portion of North America. We summarized multi-year, multi-pixel dynamics in transition matrices, calculated IT metrics from the matrices, and used matrix projection to quantify disequilibrium dynamics and long-term trajectories of the metrics. Results: Mapping the IT metrics and their disequilibria revealed gradients in the spatiotemporal complexity and organization of multi-year land surface phenology dynamics at continental to local scales. These gradients suggest influences of biophysical and biogeographic setting, ecological development and disturbances, land use, and other drivers of landscape ecological dynamics. The spatiotemporal IT metrics were influenced by both year-to-year dynamics and spatial landscape heterogeneity, but correlations with spatial and temporal complexity measures varied among the IT metrics. Landscapes showing the strongest disequilibrium dynamics were mostly in the western part of the continent and appeared to be associated with large-scale disturbances including severe fire, forest pathogens, climate variability, and land use change—important subjects for further study. Conclusions: This approach reveals novel features of the shifting landscape mosaic, with implications for understanding landscape resilience and sustainability. Resulting spatial data products describing long-term landscape dynamics have potential applications in broad-scale ecological modeling, monitoring, assessment, and prediction.

Certain environmental processes, while influential, are inherently difficult to quantify and detect using traditional time series analyses, particularly among variables with different seasonal progressions. Disturbances that only manifest in part of a season (e.g., spring defoliation) or subtle climate shifts can pose detection challenges when they occur in the presence of other variability. Increasing sampling rates or even adding new sensors may not reveal the anticipated patterns. Eddy covariance tower data are a useful example for which various environmental drivers influence the overall signal, contributing noise and seemingly discordant variation. While eddy flux data are a rich representation of information, distinguishing expected seasonal responses within a signal can be challenging, especially where drivers may have either fast or lagged responses. A conventional solution might be to analyze and effectively smooth the data over daily to monthly intervals. However, such smoothed data will not exhibit the same variance, and subsequent regressions may not isolate relationships and anomalies to specific seasons. This paper introduces and demonstrates the use of a newly developed R software package, PolarMetrics, which is used to analyze 20 years of data from one AmeriFlux tower using a polar (circular) approach that reduces data volume to a smaller set of derived seasonal timing and magnitude metrics. Polar metrics quantify the annual cycle of input variables, and permit direct comparison of the strength and timing of seasonality. While performing the analysis over all years produces a synoptic result, analyzing year-by-year characterizes interannual variability.