Observations of vegetation phenology at regional-to-global scales provide important information regarding seasonal variation in the fluxes of energy, carbon, and water between the biosphere and the atmosphere. Numerous algorithms have been developed to estimate phenological transition dates using time series of remotely sensed spectral vegetation indices. A key challenge, however, is that different algorithms provide inconsistent results. This study provides a comprehensive comparison of start of season (SOS) and end of season (EOS) phenological transition dates estimated from 500 m MODIS data based on two widely used sources of such data: the TIMESAT program and the MODIS Global Land Cover Dynamics (MLCD) product. Specifically, we evaluate the impact of land cover class, criteria used to identify SOS and EOS, and fitting algorithm (local versus global) on the transition dates estimated from time series of MODIS enhanced vegetation index (EVI). Satellite-derived transition dates from each source are compared against each other and against SOS and EOS dates estimated from PhenoCams distributed across the Northeastern United States and Canada. Our results show that TIMESAT and MLCD SOS transition dates are generally highly correlated (r = 0.51-0.97), except in Central Canada where correlation coefficients are as low as 0.25. Relative to SOS, EOS comparison shows lower agreement and higher magnitude of deviations. SOS and EOS dates are impacted by noise arising from snow and cloud contamination, and there is low agreement among results from TIMESAT, the MLCD product, and PhenoCams in vegetation types with low seasonal EVI amplitude or with irregular EVI time series. In deciduous forests, SOS dates from the MLCD product and TIMESAT agree closely with SOS dates from PhenoCams, with correlations as high as 0.76. Overall, our results suggest that TIMESAT is well-suited for local-to-regional scale studies because of its ability to tune algorithm parameters, which makes it more flexible than the MLCD product. At large spatial scales, where local tuning is not feasible, the MLCD product provides a readily available data set based on a globally consistent approach that provides SOS and EOS dates that are comparable to results from TIMESAT.

Phenology is a valuable diagnostic of ecosystem health, and has applications to environmental monitoring and management. Here, we conduct an intercomparison analysis using phenological transition dates derived from near-surface PhenoCam imagery and MODIS satellite remote sensing. We used approximately 600 site-years of data, from 128 camera sites covering a wide range of vegetation types and climate zones. During both “greenness rising” and “greenness falling” transition phases, we found generally good agreement between PhenoCam and MODIS transition dates for agricultural, deciduous forest, and grassland sites, provided that the vegetation in the camera field of view was representative of the broader landscape. The correlation between PhenoCam and MODIS transition dates was poor for evergreen forest sites. We discuss potential reasons (including sub-pixel spatial heterogeneity, flexibility of the transition date extraction method, vegetation index sensitivity in evergreen systems, and PhenoCam geolocation uncertainty) for varying agreement between time series of vegetation indices derived from PhenoCam and MODIS imagery. This analysis increases our confidence in the ability of satellite remote sensing to accurately characterize seasonal dynamics in a range of ecosystems, and provides a basis for interpreting those dynamics in the context of tangible phenological changes occurring on the ground.

Near surface (i.e., camera) and satellite remote sensing metrics have become widely used indicators of plant growing seasons. While robust linkages have been established between field metrics and ecosystem exchange in many land cover types, assessment of how well remotely-derived season start and end dates depict field conditions in arid ecosystems remain unknown. We evaluated the correspondence between field measures of start (SOS; leaves unfolded and canopy greenness >0) and end of season (EOS) and canopy greenness for two widespread species in southwestern U.S. ecosystems with those metrics estimated from near-surface cameras and MODIS NDVI for five years (2012–2016). Using Timesat software to estimate SOS and EOS from the phenocam green chromatic coordinate (GCC) greenness index resulted in good agreement with ground observations for honey mesquite but not black grama. Despite differences in the detectability of SOS and EOS for the two species, GCC was significantly correlated with field estimates of canopy greenness for both species throughout the growing season. MODIS NDVI for this arid grassland site was driven by the black grama signal although a mesquite signal was discernable in average rainfall years. Our findings suggest phenocams could help meet myriad needs in natural resource management.

Vegetation phenology controls the seasonality of many ecosystem processes, as well as numerous biosphere-atmosphere feedbacks. Phenology is also highly sensitive to climate change and variability. Here we present a series of datasets, together consisting of almost 750 years of observations, characterizing vegetation phenology in diverse ecosystems across North America. Our data are derived from conventional, visible-wavelength, automated digital camera imagery collected through the PhenoCam network. For each archived image, we extracted RGB (red, green, blue) colour channel information, with means and other statistics calculated across a region-of-interest (ROI) delineating a specific vegetation type. From the high-frequency (typically, 30 min) imagery, we derived time series characterizing vegetation colour, including “canopy greenness”, processed to 1- and 3-day intervals. For ecosystems with one or more annual cycles of vegetation activity, we provide estimates, with uncertainties, for the start of the “greenness rising” and end of the “greenness falling” stages. The database can be used for phenological model validation and development, evaluation of satellite remote sensing data products, benchmarking earth system models, and studies of climate change impacts on terrestrial ecosystems.

Scientists gathered at a workshop in Cambridge, Mass., last June to identify opportunities and challenges associated with integrating multiscale, multiplatform streams of data to produce higher-level phenological data products (e.g., models) and applications at a variety of spatial and temporal resolutions.

Climate change is causing an increase in the amount of forested area burned by wildfires in the western U.S. The warm, dry post-fire conditions of the region may limit tree regeneration in some areas, potentially causing a shift to non-forest vegetation. Managers are increasingly challenged by the combined impacts of greater wildfire activity, the significant uncertainty about whether forests will recover, and limited resources for reforestation efforts. Simultaneously, there has been an increased focus on post-fire reforestation efforts as tree planting has become a popular climate change mitigation strategy across the nation. Therefore, with increased interest and need, it is crucial to identify where varying approaches to support post-fire tree regeneration are most likely to be successful.   This project seeks to help managers target and prioritize various post-fire management approaches and identify the areas where these actions will promote recovery and adaptation or will be less successful due to changing climate conditions. Researchers will quantify how post-fire climate conditions affect both natural and assisted tree regeneration. Then, this information will be used to make a freely available web tool that will predict the probability of post-fire regeneration in recently affected areas for three dominant conifer species: ponderosa pine, Douglas-fir, and western larch. This tool will be applied in collaboration with managers from the Bureau of Land Management and The Nature Conservancy to help prioritize planting efforts on a recent wildfire in Montana. This planting effort will provide an opportunity to test if planting seedlings from warmer and drier areas may allow for adaptation to the warming climate conditions. Combined, the work will help managers to effectively use limited resources by prioritizing where and how to plant seedlings and promote forest regeneration after wildfires. 

Native American tribes are interested in managing their homelands for future generations, using both Indigenous and western science to make decisions in culturally appropriate ways. In particular, there is interest in strategic grazing management as a natural climate solution to strengthen the resilience of grasslands to a changing climate. This includes the restoration of free-ranging bison as well as the management of cattle (and domestic bison) in ways that approximate wild bison grazing behavior, to capture similar ecological and climate change benefits.   Despite the growing interest in grazing management as a tool for grassland resilience and soil health, there has not been a systematic synthesis that directly relate to bison and cattle management decisions being made by Tribes and First Nations. Furthermore, the existing evidence is framed from a western scientific perspective and does not account for the rich knowledge of Indigenous science and cultural practice. Given the growing movement for Indigenous-held lands to be managed in culturally-appropriate ways, it is crucial that efforts to develop management recommendations take both Indigenous and western science into account.   To address these needs, the Wildlife Conservation Society and the Blackfeet Nation are partnering to launch an Indigenous Scholars Hub that will bring together Blackfeet Nation decision makers and Indigenous graduate students to: 1) co-create a synthesis and future research plan on bison and cattle grazing as a tool for climate adaptation and 2) link Indigenous and western science on grazing to inform on-going land use planning, bison restoration, and cattle grazing management decisions. Results of this review will be shared with other Native American tribes also interested in the topic.  The Indigenous Scholars Hub will be a pilot for weaving together Indigenous and western science, provide key information for decision-makers, and create a mentoring networking to support early career Indigenous researchers who wish to contribute to durable conservation of their homelands. 

Pinyon-juniper woodlands are important ecosystems in the western U.S. that provide numerous critical environmental, economic, and cultural benefits. For example, pinyon pines are a significant cultural resource for multiple Native American Tribes and provide necessary habitat for plants and wildlife (including at risk species, such as the pinyon-jay). Despite their importance, stress put on pinyon-juniper woodlands by wildfires and other interacting effects of climate change are causing major population declines of these woodland trees. Such changes to pinyon-juniper woodlands lead to uncertainty for land managers on best practices for protecting these ecosystems from stand replacing fire (where most or all of the trees are killed), and restoring pinyon-juniper communities when fire does occur. To address these uncertainties, researchers are collaborating with a diverse set of land managers, scientists and tribal partners to answer two questions: (1) How does a holistic understanding of the ways tree thinning and fire affect pinyon-juniper woodlands lead to improved management options? and (2) What innovative restoration techniques can restore pinyon-juniper communities following fire in the face of climate change? The research team will use long-term observational data and sites managed by federal and tribal partners to explore ecosystem health and regeneration patterns over pinyon-juniper woodlands that have experienced thinning or fire. This will include assessments of rare and threatened plant species. The researchers will also test a suite of novel restoration options following past fires to provide tools for pinyon-juniper restoration success in places where natural post-fire regrowth is not occurring. Taken together, this inclusive research project will address some of the most pressing resource management information needs in order to develop strategies to sustain pinyon-juniper woodlands and the many services they provide.

Forested areas in the Western U.S. that are impacted by disturbances such as fire and drought have increased in recent decades. This trend is likely to continue, with the increase in frequency and extent of wildfire activity being especially concerning. Resource managers need reliable scientific information to better understand wildfire occurrence, which can vary substantially across landscapes and throughout time. However, few scientific models capture this variability, and projections of future potential changes in fire occurrence can include some uncertainty. This uncertainty can limit our ability to anticipate potential wildfire impacts on society and ecological systems. Another method to help managers prepare for the future is to examine post-fire conditions and asses how and if forests might transition to different landscape types after wildfires (e.g. a change from conifer to deciduous forest). Some studies show that post-fire tree regeneration has been limited in many of the areas burned, especially in large high-severity patches, changing the composition of the landcover. However, it is also unclear how common this post-fire state transition is and what thresholds (e.g., fire severity, burn patch size, post-fire weather conditions) predict such transitions. This research will investigate the impacts that fire disturbances and drought have on the structure and composition of forest ecosystems across the Western U.S. There will be three main areas of focus: 1) simulating interactions among climate, drought, vegetation, and disturbances, like fire; 2) monitoring and predicting post-fire forest vegetation recovery using remote sensing and simulation models, and 3) modeling wildfire occurrence and risk using historical data. This project builds off work previously done under the former USGS LandCarbon program.   Products from this project will be used to assess past patterns of wildfire risk to homes and project future potential changes in fire occurrence and risk across the conterminous U.S. Outputs from this project will also inform fire management decision-making and can also be used to advance existing predictive technology, including landscape simulation models such as LANDIS-II, to help resource manager better prepare for future conditions.