Forest phenoclusters for Argentina based on vegetation phenology and climate

2022 ◽  
Author(s):  
Eduarda M. O. Silveira ◽  
Volker C. Radeloff ◽  
Guillermo J. Martínez Pastur ◽  
Sebastián Martinuzzi ◽  
Natalia Politi ◽  
...  
Keyword(s):  
Vegetation Phenology

scholarly journals Winter snow and spring temperature have differential effects on vegetation phenology and productivity across plant communities

2020 ◽  
Author(s):  
Katharine C. Kelsey ◽  
Stine Højlund Pedersen ◽  
A. Joshua Leffler ◽  
Joseph O. Sexton ◽  
Min Feng ◽  
...  
Keyword(s):  
Plant Communities ◽  
Vegetation Phenology ◽  
Spring Temperature ◽  
Differential Effects ◽  
Winter Snow

scholarly journals Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

PLoS ONE ◽  
2015 ◽  
Vol 10 (3) ◽  
pp. e0119811 ◽  
Author(s):  
Sofia Bajocco ◽  
Eleni Dragoz ◽  
Ioannis Gitas ◽  
Daniela Smiraglia ◽  
Luca Salvati ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Phenology ◽  
Forest Fuels ◽  
Coarse Resolution

scholarly journals Spectral-Temporal Classification Using Vegetation Phenology

1980 ◽  
Vol GE-18 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Robert M. Haralick ◽  
Christine A. Hlavka ◽  
Ryuzo Yokoyama ◽  
S. M. Carlyle
Keyword(s):  
Vegetation Phenology

scholarly journals Interactive coupling of regional atmosphere with biosphere in the new generation regional climate system model REMO-iMOVE

2014 ◽  
Vol 7 (3) ◽  
pp. 1093-1114 ◽  
Author(s):  
C. Wilhelm ◽  
D. Rechid ◽  
D. Jacob
Keyword(s):  
Land Surface ◽  
Regional Climate ◽  
Regional Model ◽  
Horizontal Resolution ◽  
Soil Conditions ◽  
Data Sets ◽  
Vegetation Phenology ◽  
Land Surface Scheme ◽  
New Model ◽  
Surface Scheme

Abstract. The main objective of this study is the coupling of the regional climate model REMO with a new land surface scheme including dynamic vegetation phenology, and the evaluation of the new model version called REMO with interactive MOsaic-based VEgetation: REMO-iMOVE. First, we focus on the documentation of the technical aspects of the new model constituents and the coupling mechanism. The representation of vegetation in iMOVE is based on plant functional types (PFTs). Their geographical distribution is prescribed to the model which can be derived from different land surface data sets. Here, the PFT distribution is derived from the GLOBCOVER 2000 data set which is available on 1 km × 1 km horizontal resolution. Plant physiological processes like photosynthesis, respiration and transpiration are incorporated into the model. The vegetation modules are fully coupled to atmosphere and soil. In this way, plant physiological activity is directly driven by atmospheric and soil conditions at the model time step (two minutes to some seconds). In turn, the vegetation processes and properties influence the exchange of substances, energy and momentum between land and atmosphere. With the new coupled regional model system, dynamic feedbacks between vegetation, soil and atmosphere are represented at regional to local scale. In the evaluation part, we compare simulation results of REMO-iMOVE and of the reference version REMO2009 to multiple observation data sets of temperature, precipitation, latent heat flux, leaf area index and net primary production, in order to investigate the sensitivity of the regional model to the new land surface scheme and to evaluate the performance of both model versions. Simulations for the regional model domain Europe on a horizontal resolution of 0.44° had been carried out for the time period 1995–2005, forced with ECMWF ERA-Interim reanalyses data as lateral boundary conditions. REMO-iMOVE is able to simulate the European climate with the same quality as the parent model REMO2009. Differences in near-surface climate parameters can be restricted to some regions and are mainly related to the new representation of vegetation phenology. The seasonal and interannual variations in growth and senescence of vegetation are captured by the model. The net primary productivity lies in the range of observed values for most European regions. This study reveals the need for implementing vertical soil water dynamics in order to differentiate the access of plants to water due to different rooting depths. This gets especially important if the model will be used in dynamic vegetation studies.

scholarly journals Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography

2016 ◽  
Vol 13 (17) ◽  
pp. 5085-5102 ◽  
Author(s):  
Caitlin E. Moore ◽  
Tim Brown ◽  
Trevor F. Keenan ◽  
Remko A. Duursma ◽  
Albert I. J. M. van Dijk ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Tropical Rainforests ◽  
Vegetation Phenology ◽  
Repeat Photography ◽  
Tropical Savannas ◽  
Diverse Range ◽  
Canopy Development ◽  
Continental Scale ◽  
Digital Repeat Photography

Abstract. Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).

scholarly journals Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Andrew D. Richardson ◽  
Koen Hufkens ◽  
Tom Milliman ◽  
Donald M. Aubrecht ◽  
Min Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Type ◽  
Digital Camera ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Climate Change Impacts ◽  
Terrestrial Ecosystems ◽  
Vegetation Phenology ◽  
Phenological Model ◽  
Vegetation Activity

Abstract 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.

Sign in / Sign up

Export Citation Format

Share Document