Using time series of MODIS land surface phenology to model temperature and photoperiod controls on spring greenup in North American deciduous forests

The timing of leaf emergence in temperate and boreal forests is changing, which has profound implications for a wide array of ecosystem processes and services. Spring phenology models, which have been widely used to predict the timing of leaf emergence, generally assume that a combination of photoperiod and thermal forcing control when leaves emerge. However, the exact nature and magnitude of how photoperiod and temperature individually and jointly control leaf emergence is the subject of ongoing debate. Here we use a continuous development model in combination with time series of land surface phenology measurements from MODIS to quantify the relative importance of photoperiod and thermal forcing in controlling the timing of canopy greenup in eastern temperate and boreal forests of North America. The model accurately predicts biogeographic and interannual variation in the timing of greenup across the study region (median RMSE = 4.6 days, median bias = 0.30 days). Results reveal strong biogeographic variation in the period prior to greenup when temperature and photoperiod influence greenup that covaries with the importance of photoperiod versus thermal controls. Photoperiod control on leaf emergence is dominant in warmer climates, but exerts only modest influence on the timing of leaf emergence in colder climates. Results from models estimated using ground-based observations of cloned lilac are consistent with those from remote sensing, which supports the realism of remote sensing-based models. Overall, results from this study suggest that apparent changes in the sensitivity of trees to temperature are modest and reflect a trade-off between decreased sensitivity to temperature and increased photoperiod control, and identify a transition in the relative importance of temperature versus photoperiod near the 10 °C isotherm in mean annual temperature. This suggests that the timing of leaf emergence will continue to move earlier as the climate warms, and that the magnitude of change will be more pronounced in colder regions with mean annual temperatures below 10 °C.Accepted manuscrip

Seasonal variation in the canopy color of temperate evergreen conifer forests

Evergreen conifer forests are the most prevalent land cover type in North America. Seasonal changes in the color of evergreen forest canopies have been documented with near‐surface remote sensing, but the physiological mechanisms underlying these changes, and the implications for photosynthetic uptake, have not been fully elucidated. Here, we integrate on‐the‐ground phenological observations, leaf‐level physiological measurements, near surface hyperspectral remote sensing and digital camera imagery, tower‐based CO₂ flux measurements, and a predictive model to simulate seasonal canopy color dynamics. We show that seasonal changes in canopy color occur independently of new leaf production, but track changes in chlorophyll fluorescence, the photochemical reflectance index, and leaf pigmentation. We demonstrate that at winter‐dormant sites, seasonal changes in canopy color can be used to predict the onset of canopy‐level photosynthesis in spring, and its cessation in autumn. Finally, we parameterize a simple temperature‐based model to predict the seasonal cycle of canopy greenness, and we show that the model successfully simulates interannual variation in the timing of changes in canopy color. These results provide mechanistic insight into the factors driving seasonal changes in evergreen canopy color and provide opportunities to monitor and model seasonal variation in photosynthetic activity using color‐based vegetation indices

Standardized NEON organismal data for biodiversity research

Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high-quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators\u27 workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open-source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks

Evergreen broadleaf greenness and its relationship with leaf flushing, aging, and water fluxes

13 Pág. Departamento de Medio Ambiente y Agronomía​ (INIA)Remote sensing capabilities to monitor evergreen broadleaved vegetation are limited by the low temporal variability in the greenness signal. With canopy greenness computed from digital repeat photography (PhenoCam), we investigated how canopy greenness related to seasonal changes in leaf age and traits as well as variation of trees’ water fluxes (characterized by sap flow and canopy conductance). The results showed that sprouting leaves are mainly responsible for the rapid increase in canopy green chromatic coordinate (GCC) in spring. We found statistically significantly differences in leaf traits and spectral properties among leaves of different leaf ages. Specifically, mean GCC of young leaves was 0.385 ± 0.010 (mean ± SD), while for mature and old leaves was 0.369 ± 0.003, and 0.376 ± 0.004, respectively. Thus, the temporal dynamics of canopy GCC can be explained by changes in leaf spectral properties and leaf age. Sap flow and canopy conductance are both well explained by a combination of environmental drivers and greenness (96% and 87% of the variance explained, respectively). In particular, air temperature and vapor pressure deficit (VPD) explained most of sap flow and canopy conductance variance, respectively. Besides, GCC is an important explanatory variable for variation of canopy conductance may because GCC can represent the leaf ontogeny information. We conclude that PhenoCam GCC can be used to identify the leaf flushing for evergreen broadleaved trees, which carries important information about leaf ontogeny and traits. Thus, it can be helpful for better estimating canopy conductance which constraints water fluxes.The authors acknowledge the Alexander von Humboldt Foundation for supporting this research with the Max Planck Prize to Markus Reichstein. Yunpeng Luo and Mirco Migliavacca gratefully acknowledge the financial support from the China Scholarship Council. ADR acknowledges support for the PhenoCam network from the National Science Foundation ( DEB- 1702697 ). Javier Pacheco-Labrador and Mirco Migliavacca acknowledge the German Aerospace Center (DLR) project OBEF-Accross2 “The Potential of Earth Observations to Capture Patterns of Biodiversity” (Contract No. 50EE1912). The research also received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 721995 and Ministerio de Economíay Competitividad through FLUXPEC CGL2012-34383 and SynerTGE CGL2015-G9095-R (MINECO/FEDER, UE) projects.Peer reviewe

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

GaRM: GaRM: A Forest Gap Radiation Model

GaRM simulates spatially distributed radiation in circular clearings that are surrounded by a homogeneous dense forest. The model accounts for modification of radiation on the forest gap floor due to topographic characteristics such as slope and aspect, and for forest characteristics including canopy height and density and gap size

FoRM: A physically based forest radiation model

A physically based forest radiation model (FoRM) was developed to quantify net radiation on the forest floor and its dependence on vegetation density in midlatitude coniferous forests

drawROI: An interactive toolkit to extract phenological time series data from digital repeat photography

Here, we introduce and present "DrawROI" as an interactive web-based application for imagery from PhenoCam. DrawROI can also be used offline, as a fully independent toolkit that significantly facilitates extraction of phenological data from any stack of digital repeat photography images. DrawROI provides a responsive environment for phenological scientists to interactively a) delineate ROIs, b) handle field of view (FOV) shifts, and c) extract and export time series data characterizing image color (i.e. red, green and blue channel digital numbers for the defined ROI). The application utilizes artificial intelligence and advanced machine learning techniques and gives user the opportunity to redraw new ROIs every time an FOV shift occurs. DrawROI also offers a quality control flag to indicate noisy data and images with low quality due to presence of foggy weather or snow conditions. The web-based application significantly accelerates the process of creating new ROIs and modifying pre-existing ROI in the PhenoCam database. The offline toolkit is presented as an open source R-package that can be used with similar datasets with time-lapse photography to obtain more data for studying phenology for a large community of ecologists

calcSolar: Solar position and radiation components

This package provide many functions to estimate sun position in the sky, and direct and diffuse solar radiation components