Climate and Management Practices Jointly Control Vegetation Phenology in Native and Introduced Prairie Pastures

Climate, human disturbances, and management practices jointly control the spatial and temporal patterns of land surface phenology. However, most studies solely focus on analyzing the climatic controls on the inter-annual variability and trends in vegetation phenology. Investigating the main and interacting effects of management practices and climate might be crucial in determining vegetation phenology and productivity. This study examined the impacts of climate and management practices on vegetation phenology and productivity in adjacent native and introduced prairie pastures, which have detailed long-term management records, by combining climate, management, and satellite remote sensing data (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat). Modeled gross primary production (GPP) using vegetation photosynthesis model (VPM) was also included to investigate the dynamics of productivity. When comparing the impacts of the same management practices on different pastures, we used paired comparison, namely, comparing the native and introduced prairies side by side in the same year. The interactions of management practices and climate were investigated through comparing years with similar management but different climate (e.g., years with rainfall or not following baling events) in the same pasture. Results showed that air temperature (Ta) was an important factor in determining the start of the season (SOS) and the length of the season (LOS). Total rainfall (RF) during the annual growing season (AGS, derived from vegetation indices (VIs)) had the largest explanatory power (R2 = 0.53) in explaining the variations in the seasonal sums of VIs. The variations in GPP were better explained by RF (R2 = 0.43) than Ta (R2 = 0.14). Using the thermal growing season (March–October) or AGS climate factors did not show large differences in determining the relationships between phenology, GPP, and climate factors. Drought shortened the LOS and decreased GPP. In terms of management practices, grazing generally reduced the VIs and burning induced early greening-up and enhanced vegetation growth. Drought plus other management practices (e.g., grazing or baling) greatly affected vegetation phenology and suppressed GPP. The negative impacts (i.e., removal of biomass) of grazing on vegetation was compensated by enhanced vegetation growth after good RF. This study demonstrated that the interactions of climate and management practices could be positive (burning plus baling in a good RF year) or negative (grazing/baling plus drought), and can significantly affect vegetation phenology and production.

[1]  P. Gowda,et al.  Dormant Season Vegetation Phenology and Eddy Fluxes in Native Tallgrass Prairies of the U.S. Southern Plains , 2022, Remote. Sens..

[2]  P. Gowda,et al.  The potential of active and passive remote sensing to detect frequent harvesting of alfalfa , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[3]  P. Gowda,et al.  Differential responses of native and managed prairie pastures to environmental variability and management practices , 2020 .

[4]  P. Gowda,et al.  Burning and Climate Interactions Determine Impacts of Grazing on Tallgrass Prairie Systems☆ , 2020, Rangeland Ecology and Management.

[5]  Prasanna H. Gowda,et al.  Response of Tallgrass Prairie to Management in the U.S. Southern Great Plains: Site Descriptions, Management Practices, and Eddy Covariance Instrumentation for a Long-Term Experiment , 2019, Remote. Sens..

[6]  P. Gowda,et al.  Carbon dioxide and water vapor fluxes in winter wheat and tallgrass prairie in central Oklahoma. , 2018, The Science of the total environment.

[7]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[8]  Martha C. Anderson,et al.  Quantifying agricultural drought in tallgrass prairie region in the U.S. Southern Great Plains through analysis of a water-related vegetation index from MODIS images , 2017 .

[9]  Russell G. Congalton,et al.  MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types , 2017 .

[10]  John S. Kimball,et al.  A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States , 2017, Remote. Sens..

[11]  Prasanna H. Gowda,et al.  Examining the short-term impacts of diverse management practices on plant phenology and carbon fluxes of Old World bluestems pasture , 2017 .

[12]  Tsegaye Tadesse,et al.  A hybrid approach for detecting corn and soybean phenology with time-series MODIS data , 2016 .

[13]  L. Vermeire,et al.  The effect of fire intensity, nutrients, soil microbes, and spatial distance on grassland productivity , 2016, Plant and Soil.

[14]  Jie Wang,et al.  Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Dailiang Peng,et al.  Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America , 2016 .

[16]  Tsegaye Tadesse,et al.  Assessing the evolution of soil moisture and vegetation conditions during the 2012 United States flash drought , 2016 .

[17]  P. Ciais,et al.  Biomass production efficiency controlled by management in temperate and boreal ecosystems , 2015 .

[18]  J. Basara,et al.  Drought and Pluvial Dipole Events within the Great Plains of the United States , 2015 .

[19]  Nuno Carvalhais,et al.  Codominant water control on global interannual variability and trends in land surface phenology and greenness , 2015, Global change biology.

[20]  Amanda M. Schwantes,et al.  Global satellite monitoring of climate-induced vegetation disturbances. , 2015, Trends in plant science.

[21]  Jinwei Dong,et al.  Sensitivity of vegetation indices and gross primary production of tallgrass prairie to severe drought , 2014 .

[22]  Scott L. Powell,et al.  Bringing an ecological view of change to Landsat‐based remote sensing , 2014 .

[23]  I. Wing,et al.  Net carbon uptake has increased through warming-induced changes in temperate forest phenology , 2014 .

[24]  M. Hoerling,et al.  Causes and Predictability of the 2012 Great Plains Drought , 2014 .

[25]  Clement Atzberger,et al.  Phenological Metrics Derived over the European Continent from NDVI3g Data and MODIS Time Series , 2013, Remote. Sens..

[26]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[27]  J. Basara,et al.  Drought and Associated Impacts in the Great Plains of the United States—A Review , 2013 .

[28]  D. Engle,et al.  The rising Great Plains fire campaign: citizens' response to woody plant encroachment , 2013 .

[29]  Jinwei Dong,et al.  Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011 , 2013, Proceedings of the National Academy of Sciences.

[30]  David P. Billesbach,et al.  Carbon, water, and heat flux responses to experimental burning and drought in a tallgrass prairie , 2012 .

[31]  K. Beurs,et al.  Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology , 2012 .

[32]  Robert E. Wolfe,et al.  An Enhanced TIMESAT Algorithm for Estimating Vegetation Phenology Metrics From MODIS Data , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Rob J Hyndman,et al.  Phenological change detection while accounting for abrupt and gradual trends in satellite image time series , 2010 .

[34]  N. Buchmann,et al.  Management and climate impacts on net CO2 fluxes and carbon budgets of three grasslands along an elevational gradient in Switzerland , 2010 .

[35]  M. Friedl,et al.  Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product , 2010 .

[36]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[37]  Steven W. Running,et al.  Testing a MODIS Global Disturbance Index across North America , 2009 .

[38]  José A. Sobrino,et al.  Global land surface phenology trends from GIMMS database , 2009 .

[39]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[40]  James P. Verdin,et al.  Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data , 2008 .

[41]  J. Mustard,et al.  Cross-scalar satellite phenology from ground, Landsat, and MODIS data , 2007 .

[42]  Sutherland,et al.  Statewide Monitoring of the Mesoscale Environment: A Technical Update on the Oklahoma Mesonet , 2007 .

[43]  James P. Verdin,et al.  A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States , 2007 .

[44]  Mark A. Friedl,et al.  Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements , 2006 .

[45]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[46]  S. Goetz,et al.  Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[47]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[48]  N. Delbart,et al.  Determination of phenological dates in boreal regions using normalized difference water index , 2005 .

[49]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[50]  Alan H. Strahler,et al.  Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments , 2005 .

[51]  R. Siegwolf,et al.  Effect of land management on ecosystem carbon fluxes at a subalpine grassland site in the Swiss Alps , 2005 .

[52]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[53]  Randal D. Koster,et al.  On the Cause of the 1930s Dust Bowl , 2004, Science.

[54]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[55]  S. Verma,et al.  Interannual variability in net CO2 exchange of a native tallgrass prairie , 2003 .

[56]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[57]  Annette Menzel,et al.  Phenology: Its Importance to the Global Change Community , 2002 .

[58]  D. Brockway,et al.  Restoring fire as an ecological process in shortgrass prairie ecosystems: initial effects of prescribed burning during the dormant and growing seasons. , 2002, Journal of environmental management.

[59]  W. Cohen,et al.  Characterizing 23 Years (1972–95) of Stand Replacement Disturbance in Western Oregon Forests with Landsat Imagery , 2002, Ecosystems.

[60]  J. Peñuelas,et al.  Responses to a Warming World , 2001, Science.

[61]  Kevin P. Price,et al.  Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains , 2001 .

[62]  K. Hibbard,et al.  A Global Terrestrial Monitoring Network Integrating Tower Fluxes, Flask Sampling, Ecosystem Modeling and EOS Satellite Data , 1999 .

[63]  F. Woodward,et al.  Dynamic responses of terrestrial ecosystem carbon cycling to global climate change , 1998, Nature.

[64]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[65]  G. Dedieu,et al.  Global-Scale Assessment of Vegetation Phenology Using NOAA/AVHRR Satellite Measurements , 1997 .

[66]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[67]  Maosheng Zhao,et al.  A new satellite-based methodology for continental-scale disturbance detection. , 2007, Ecological applications : a publication of the Ecological Society of America.

[68]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .