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.

Abstract (from DigitalCommons@University of Nebraska - Lincoln): Native American peoples of the Northern and Central Plains have long endured harsh climate conditions, such as floods and droughts, and they possess valuable traditional knowledges that have enhanced their resilience to these extreme events. However, in recent times, limited capacity to adapt to a rapidly changing climate combined with a lack of resources have increased tribes’ vulnerability to climate extremes and their associated impacts. In response, a number of projects have been developed to assist tribes with their self-identified climate- and drought-related needs, particularly in the context of on-reservation decision-making. In this case study, we present an engagement strategy that was piloted for the tribes of the Wind River Indian Reservation in Wyoming and replicated for other tribes across the Northern and Central Plains. We found that frequent, face-to-face interactions between tribal and scientific communities builds relationships and trust between these two groups. We also found that climate capacity-building projects that include a diverse team of physical and social scientists, as well as tribal members, provide the greatest benefit to tribes. Finally, we found that these capacity-building projects can help reinforce tribal sovereignty.

Abstract (from PNAS): Recent decades have seen droughts across multiple US river basins that are unprecedented over the last century and potentially longer. Understanding the drivers of drought in a long-term context requires extending instrumental data with paleoclimatic data. Here, a network of new millennial-length streamflow reconstructions and a regional temperature reconstruction from tree rings place 20th and early 21st century drought severity in the Upper Missouri River basin into a long-term context. Across the headwaters of the United States’ largest river basin, we estimated region-wide, decadal-scale drought severity during the “turn-of-the-century drought” ca. 2000 to 2010 was potentially unprecedented over the last millennium. Warming temperatures have likely increasingly influenced streamflow by decreasing runoff efficiency since at least the late 20th century.

One of the biggest challenges facing resource managers today is not knowing exactly when, where, or how climate change effects will unfold. To help federal land managers address this need, the North Central Climate Adaptation Science Center (NC CASC) has been working with the National Park Service (NPS) to pioneer an approach for incorporating climate science and scenario planning into NPS planning processes, in particular Resource Stewardship Strategies (RSS). These strategies serve as a long-range planning tool for a national park unit to achieve its desired natural and cultural resource conditions, and are used to guide a park’s full spectrum of resource-specific management plans and day-to-day management activities. To support adaptation planning within national parks, a previous NC CASC project designed an approach for integrating climate science and scenarios into the RSS process using Devils Tower National Monument in Wyoming as a test case. Building on these efforts, the present project applied the lessons drawn from the Devils Tower experience to a different NPS unit and context – Wind Cave National Park in South Dakota. This additional work was important to ensure that findings are relevant to multiple contexts, because RSSs are a cornerstone of NPS planning and are being completed for all NPS units. Not only did this work result in climate-informed resource management goals and actions for Wind Cave (documented as part of the RSS summary document and dynamic RSS database), but it also enabled researchers to refine and publish guidance for incorporating climate science and scenario planning into the RSS process (“Supplemental Guidance: Integration of Climate Change Scenario Planning into the Resource Stewardship Strategy Process”, NPS in press). This RSS supplemental guidance will inform upcoming RSS efforts, including those for Yellowstone National Park and Apostle Islands National Lakeshore. 

This document is a companion to the Resource Stewardship Strategy Development Guide, developed in 2019. This document provides a guide to more thoroughly address climate change in resource stewardship strategies through scenario planning. Scenario planning enables stakeholders to identify key climate sensitivities in resources and management concerns, examine a range of relevant and plausible future conditions, and explore management options that can be appropriate and effective across a range of potential futures. The intent of this guidance is to provide a repeatable methodology that the National Park Service can use to better incorporate scenarios and climate science into resource stewardship strategies.