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.

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.

Monitoring the terrestrial carbon cycle for responses to disturbances, caused for example by extreme climate events and insect outbreaks, has the potential to provide early warnings about ecosystem change. However, our capability to detect these carbon balance responses by atmospheric CO₂ monitoring remains unknown despite sub-ppm comparability of many well-calibrated CO₂ measurement sites. Here, this study explores how accurately atmospheric CO₂ and transport models can detect imposed carbon flux anomalies against a background terrestrial flux. Air mass back trajectories from three CO₂ monitoring stations in the central U.S. Rocky Mountains for one year (2008) were computationally simulated. To simulate reduced CO₂ uptake, a constant +0.2 µmol C m⁻¹ s⁻² anomaly was added to all surface fluxes within perturbation domains of varying size. A spatially and temporally uniform 10°×10° +0.2 µmol C m⁻² s⁻¹ flux anomaly (+6 Tg C m⁻¹) was detectable above a comprehensive model-data mismatch detection threshold in a large majority of months at each site, but only when the perturbation was located in the central Mountain West. The intensity of the perturbation and its area were important to detection, but the effect of area declined exponentially with increasing source-to-station distance. To further evaluate response, a more realistic spatiotemporally varying drought extracted from a dynamic global vegetation model with a monthly varying perturbation area (1°×1° to 5°×5°) and higher peak intensity (+0.8 µmol C m⁻² s⁻¹) was applied. Detectability of excess CO₂ from this experiment by the nearest CO₂ site (Utah) was similar to detectability of the largest (10°×10°) uniform perturbation. These experiments demonstrate disturbance and drought related carbon-cycle perturbations do create a discernible impact on the composite signal of atmospheric CO₂ if sufficiently proximal to a measurement station.

Achieving net-zero greenhouse gas emissions may require large-scale deployment of direct air capture (DAC). However, the least-cost locations for deployment, in addition to the preferred coupling of capture technology and energy supply, remain unclear. Here, we develop a spatially-explicit techno-economic model of liquid solvent and solid sorbent DAC in each U.S. census tract. Mean unsubsidized costs across tracts decline from $205–$450 tCO2-1 in 2035 to $165–$346 tCO2-1 in 2050, with energy comprising 29-86% of costs. Liquid solvent DAC with grid electricity is the most preferred configuration nationally, although there is substantial spatial heterogeneity and uncertainty. Tax credits reduce private costs and shift preferences toward co-located energy supply. Spatial heterogeneity is driven by climate, energy prices, and grid emissions intensities, while uncertainty in costs and technology preferences is driven by learning rates and public policy. These findings provide a foundation to guide DAC deployment strategies and place-based policy design.