Temporal Greenness Trends in Stable Natural Land Cover and Relationships with Climatic Variability across the Conterminous United States

Lei Ji aASRC Federal Data Solutions, Sioux Falls, South Dakota

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Jesslyn F. Brown bEarth Resources Observation and Science Center, U.S. Geological Survey, Sioux Falls, South Dakota

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Abstract

Assessment of temporal trends in vegetation greenness and related influences aids understanding of recent changes in terrestrial ecosystems and feedbacks from weather, climate, and environment. We analyzed 1-km normalized difference vegetation index (NDVI) time series data (1989–2016) derived from the Advanced Very High Resolution Radiometer (AVHRR) and developed growing-season time-integrated NDVI (GS-TIN) for estimating seasonal vegetation activity across stable natural land cover in the conterminous United States (CONUS). After removing areas from analysis that had experienced land-cover conversion or modification, we conducted a monotonic trend analysis on the GS-TIN time series and found that significant positive temporal trends occurred over 35% of the area, whereas significant negative trends were observed over only 3.5%. Positive trends were prevalent in the forested lands of the eastern one-third of CONUS and far northwest, as well as in grasslands in the north-central plains. We observed negative and nonsignificant trends mainly in the shrublands and grasslands across the northwest, southwest, and west-central plains. To understand the relationship of climate variability with these temporal trends, we conducted partial and multiple correlation analyses on GS-TIN, growing-season temperature, and water-year precipitation time series. The GS-TIN trends in northern forests were positively correlated with temperature. The GS-TIN trends in the central and western shrublands and grasslands were negatively correlated with temperature and positively correlated with precipitation. Our results revealed spatial patterns in vegetation greenness trends for different stable natural vegetation types across CONUS, enhancing understanding gained from prior studies that were based on coarser 8-km AVHRR data.

Significance Statement

Assessing vegetation trends, cycles, and related influences is important for understanding the responses and feedbacks of terrestrial ecosystems to climatic and environmental changes. We analyzed vegetation greenness trends (1989–2016) for stable natural land cover across the conterminous United States, based on vegetation index time series derived from coarse-resolution optical satellite sensors. We found greening trends in the forests of the east and far northwest and the grasslands of the northern central plains that correlated with increasing temperature in the regions. We observed browning and no trends mainly in the shrublands and grasslands across the northwest, southwest, and western central plains, associated with increasing temperature and decreasing precipitation. Future research should focus on vegetation greenness analysis using finer-resolution satellite data.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lei Ji, lji@contractor.usgs.gov

Abstract

Assessment of temporal trends in vegetation greenness and related influences aids understanding of recent changes in terrestrial ecosystems and feedbacks from weather, climate, and environment. We analyzed 1-km normalized difference vegetation index (NDVI) time series data (1989–2016) derived from the Advanced Very High Resolution Radiometer (AVHRR) and developed growing-season time-integrated NDVI (GS-TIN) for estimating seasonal vegetation activity across stable natural land cover in the conterminous United States (CONUS). After removing areas from analysis that had experienced land-cover conversion or modification, we conducted a monotonic trend analysis on the GS-TIN time series and found that significant positive temporal trends occurred over 35% of the area, whereas significant negative trends were observed over only 3.5%. Positive trends were prevalent in the forested lands of the eastern one-third of CONUS and far northwest, as well as in grasslands in the north-central plains. We observed negative and nonsignificant trends mainly in the shrublands and grasslands across the northwest, southwest, and west-central plains. To understand the relationship of climate variability with these temporal trends, we conducted partial and multiple correlation analyses on GS-TIN, growing-season temperature, and water-year precipitation time series. The GS-TIN trends in northern forests were positively correlated with temperature. The GS-TIN trends in the central and western shrublands and grasslands were negatively correlated with temperature and positively correlated with precipitation. Our results revealed spatial patterns in vegetation greenness trends for different stable natural vegetation types across CONUS, enhancing understanding gained from prior studies that were based on coarser 8-km AVHRR data.

Significance Statement

Assessing vegetation trends, cycles, and related influences is important for understanding the responses and feedbacks of terrestrial ecosystems to climatic and environmental changes. We analyzed vegetation greenness trends (1989–2016) for stable natural land cover across the conterminous United States, based on vegetation index time series derived from coarse-resolution optical satellite sensors. We found greening trends in the forests of the east and far northwest and the grasslands of the northern central plains that correlated with increasing temperature in the regions. We observed browning and no trends mainly in the shrublands and grasslands across the northwest, southwest, and western central plains, associated with increasing temperature and decreasing precipitation. Future research should focus on vegetation greenness analysis using finer-resolution satellite data.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Lei Ji, lji@contractor.usgs.gov
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  • Allen, C. D., and Coauthors, 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage., 259, 660684, https://doi.org/10.1016/j.foreco.2009.09.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barichivich, J., K. R. Briffa, R. B. Myneni, T. J. Osborn, T. M. Melvin, P. Ciais, S. L. Piao, and C. Tucker, 2013: Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Global Change Biol., 19, 31673183, https://doi.org/10.1111/gcb.12283.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, P. S. A., C. Atzberger, K. A. Hogda, B. Johansen, and A. K. Skidmore, 2006: Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. Remote Sens. Environ., 100, 321334, https://doi.org/10.1016/j.rse.2005.10.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bentz, B. J., and Coauthors, 2010: Climate change and bark beetles of the western United States and Canada: Direct and indirect effects. BioScience, 60, 602613, https://doi.org/10.1525/bio.2010.60.8.6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, L. T., B. E. Law, A. J. Meddens, and J. A. Hicke, 2017a: Tree mortality from fires, bark beetles, and timber harvest during a hot and dry decade in the western United States (2003–2012). Environ. Res. Lett., 12, 065005, https://doi.org/10.1088/1748-9326/aa6f94.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berner, L. T., B. E. Law, A. J., Meddens, and J. A. Hicke, 2017b: Tree mortality from fires and bark beetles at 1-km resolution, western USA, 2003–2012. ORNL DAAC, accessed 1 June 2019, https://daac.ornl.gov/VEGETATION/guides/Tree_Mortality_Western_US.html.

    • Search Google Scholar
    • Export Citation
  • Boisvenue, C., and S. W. Running, 2006: Impacts of climate change on natural forest productivity—Evidence since the middle of the 20th century. Global Change Biol., 12, 862882, https://doi.org/10.1111/j.1365-2486.2006.01134.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, J. F., B. D. Wardlow, T. Tadesse, M. J. Hayes, and B. C. Reed, 2008: The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation. GIsci. Remote Sens., 45, 1646, https://doi.org/10.2747/1548-1603.45.1.16.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, J. F., and Coauthors, 2020: Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach. Remote Sens. Environ., 238, 111356, https://doi.org/10.1016/j.rse.2019.111356.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cook, B. I., and S. Pau, 2013: A global assessment of long-term greening and browning trends in pasture lands using the GIMMS LAI3g dataset. Remote Sens., 5, 24922512, https://doi.org/10.3390/rs5052492.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daly, C., M. Halbleib, J. I. Smith, W. P. Gibson, M. K. Doggett, G. H. Taylor, J. Curtis, and P. P. Pasteris, 2008: Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol., 28, 20312064, https://doi.org/10.1002/joc.1688.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derner, J., and Coauthors, 2018: Vulnerability of grazing and confined livestock in the northern Great Plains to projected mid- and late-twenty-first century climate. Climatic Change, 146, 1932, https://doi.org/10.1007/s10584-017-2029-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • DeVries, B., M. Decuyper, J. Verbesselt, A. Zeileis, M. Herold, and S. Joseph, 2015: Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series. Remote Sens. Environ., 169, 320334, https://doi.org/10.1016/j.rse.2015.08.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • D’Odorico, P., A. Gonsamo, B. Pinty, N. Gobron, N. Coops, E. Mendez, and M. E. Schaepman, 2014: Intercomparison of fraction of absorbed photosynthetically active radiation products derived from satellite data over Europe. Remote Sens. Environ., 142, 141154, https://doi.org/10.1016/j.rse.2013.12.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Durbin, J., and G. S. Watson, 1971: Testing for serial correlation in least squares regression. III. Biometrika, 58, 119, https://doi.org/10.2307/2334313.

    • Search Google Scholar
    • Export Citation
  • Eastman, J. R., F. Sangermano, E. A. Machado, J. Rogan, and A. Anyamba, 2013: Global trends in seasonality of normalized difference vegetation index (NDVI), 1982–2011. Remote Sens., 5, 47994818, https://doi.org/10.3390/rs5104799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eidenshink, J. C., 1992: The 1990 conterminous U.S. AVHRR data set. Photogramm. Eng. Remote Sensing, 58, 809813.

  • Eidenshink, J. C., 2006: A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska. Photogramm. Eng. Remote Sensing, 72, 10271035, https://doi.org/10.14358/PERS.72.9.1027.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eidenshink, J. C., B. Schwind, K. Brewer, Z.-L. Zhu, B. Quayle, and S. Howard, 2007: A project for monitoring trends in burn severity. Fire Ecol., 3, 321, https://doi.org/10.4996/fireecology.0301003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fan, X. W., and Y. B. Liu, 2016: A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS J. Photogramm. Remote Sens., 121, 177191, https://doi.org/10.1016/j.isprsjprs.2016.09.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fry, J. A., M. J. Coan, C. G. Homer, D. K. Meyer, and J. D. Wickham, 2009: Completion of the National Land Cover Database (NLCD) 1992–2001 Land Cover Change Retrofit product. U.S. Geological Survey, 26 pp., https://pubs.usgs.gov/of/2008/1379/pdf/ofr2008-1379.pdf.

    • Search Google Scholar
    • Export Citation
  • Giglio, L., L. Boschetti, D. P. Roy, M. L. Humber, and C. O. Justice, 2018: The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ., 217, 7285, https://doi.org/10.1016/j.rse.2018.08.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gitelson, A. A., Y. Peng, and K. F. Huemmrich, 2014: Relationship between fraction of radiation absorbed by photosynthesizing maize and soybean canopies and NDVI from remotely sensed data taken at close range and from MODIS 250 m resolution data. Remote Sens. Environ., 147, 108120, https://doi.org/10.1016/j.rse.2014.02.014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gonsamo, A., J. M. Chen, and D. Lombardozzi, 2016: Global vegetation productivity response to climatic oscillations during the satellite era. Global Change Biol., 22, 34143426, https://doi.org/10.1111/gcb.13258.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, W., 2013: AVHRR Vegetation Health Product (AVHRR-VHP): User Guide. NOAA/NESDIS Center for Satellite Applications and Research, 6 pp., http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH_doc/VHP_uguide_v1.4_2013_1221.pdf.

    • Search Google Scholar
    • Export Citation
  • Hastings, D. A., and W. J. Emery, 1992: The advanced very high-resolution radiometer (AVHRR)—A brief reference guide. Photogramm. Eng. Remote Sensing, 58, 11831188.

    • Search Google Scholar
    • Export Citation
  • Havstad, K. M., J. R. Brown, R. Estell, E. Elias, A. Rango, and C. Steele, 2018: Vulnerabilities of southwestern U.S. rangeland-based animal agriculture to climate change. Climatic Change, 148, 371386, https://doi.org/10.1007/s10584-016-1834-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hicke, J. A., A. J. H. Meddens, and C. A. Kolden, 2015: Recent tree mortality in the western United States from bark beetles and forest fires. For. Sci., 62, 141153, https://doi.org/10.5849/forsci.15-086.

    • Search Google Scholar
    • Export Citation
  • Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira, 2002: Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ., 83, 195213, https://doi.org/10.1016/S0034-4257(02)00096-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • James, M. E., and S. N. V. Kalluri, 1994: The Pathfinder AVHRR land data set—An improved coarse resolution data set for terrestrial monitoring. Int. J. Remote Sens., 15, 33473363, https://doi.org/10.1080/01431169408954335.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jarque, C. M., and A. K. Bera, 1987: A test normality of observations and regression residuals. Int. Stat. Rev., 55, 163172, https://doi.org/10.2307/1403192.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ji, L., and A. J. Peters, 2003: Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens. Environ., 87, 8598, https://doi.org/10.1016/S0034-4257(03)00174-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ji, L., and J. F. Brown, 2017: Effect of NOAA satellite orbital drift on AVHRR-derived phenological metrics. Int. J. Appl. Earth Obs. Geoinf., 62, 215223, https://doi.org/10.1016/j.jag.2017.06.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, M. O., D. E. Naugle, D. Twidwell, D. R. Uden, J. D. Maestas, and B. W. Allred, 2020: Beyond inventories: Emergence of a new era in rangeland monitoring. Rangeland Ecol. Manage., 73, 577583, https://doi.org/10.1016/j.rama.2020.06.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ju, J. C., and J. G. Masek, 2016: The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data. Remote Sens. Environ., 176, 116, https://doi.org/10.1016/j.rse.2016.01.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaufmann, R. K., L. M. Zhou, Y. Knyazikhin, N. V. Shabanov, R. B. Myneni, and C. J. Tucker, 2000: Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data. IEEE Trans. Geosci. Remote Sens., 38, 25842597, https://doi.org/10.1109/36.885205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, Y., J. S. Kimball, K. Didan, and G. M. Henebry, 2014: Response of vegetation growth and productivity to spring climate indicators in the conterminous United States derived from satellite remote sensing data fusion. Agric. For. Meteor., 194, 132143, https://doi.org/10.1016/j.agrformet.2014.04.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kong, D. D., Q. Zhang, V. P. Singh, and P. J. Shi, 2017: Seasonal vegetation response to climate change in the Northern Hemisphere (1982–2013). Global Planet. Change, 148, 18, https://doi.org/10.1016/j.gloplacha.2016.10.020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Latifovic, R., D. Pouliot, and C. Dillabaugh, 2012: Identification and correction of systematic error in NOAA AVHRR long-term satellite data record. Remote Sens. Environ., 127, 8497, https://doi.org/10.1016/j.rse.2012.08.032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Y., Y. Li, S. C. Li, and S. Motesharrei, 2015: Spatial and temporal patterns of global NDVI trends: Correlations with climate and human factors. Remote Sens., 7, 13 23313 250, https://doi.org/10.3390/rs71013233.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lucht, W., and Coauthors, 2002: Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296, 16871689, https://doi.org/10.1126/science.1071828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, X. L., and Coauthors, 2013: Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect. Remote Sens. Environ., 139, 97115, https://doi.org/10.1016/j.rse.2013.07.030.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McDowell, N. G., and Coauthors, 2015: Global satellite monitoring of climate-induced vegetation disturbances. Trends Plant Sci., 20, 114123, https://doi.org/10.1016/j.tplants.2014.10.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGregor, J., and A. J. Gorman, 1994: Some considerations for using AVHRR data in climatological studies: I. Orbital characteristics of NOAA satellites. Int. J. Remote Sens., 15, 537548, https://doi.org/10.1080/01431169408954095.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meddens, A. J. H., and J. A. Hicke, 2014: Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA. For. Ecol. Manage., 322, 7888, https://doi.org/10.1016/j.foreco.2014.02.037.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miura, T., J. P. Turner, and A. R. Huete, 2013: Spectral compatibility of the NDVI across VIIRS, MODIS, and AVHRR: An analysis of atmospheric effects using EO-1 Hyperion. IEEE Trans. Geosci. Remote Sens., 51, 13491359, https://doi.org/10.1109/TGRS.2012.2224118.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Myneni, R. B., C. D. Keeling, C. J. Tucker, G. Asrar, and R. R. Nemani, 1997: Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698702, https://doi.org/10.1038/386698a0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagol, J. R., E. F. Vermote, and S. D. Prince, 2014: Quantification of impact of orbital drift on inter-annual trends in AVHRR NDVI data. Remote Sens., 6, 66806687, https://doi.org/10.3390/rs6076680.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Napton, D. E., R. F. Auch, R. M. Headley, and J. L. Taylor, 2010: Land changes and their driving forces in the southeastern United States. Reg. Environ. Change, 10, 3753, https://doi.org/10.1007/s10113-009-0084-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nash, M. S., D. F. Bradford, J. D. Wickham, and T. G. Wade, 2014: Detecting change in landscape greenness over large areas: An example for New Mexico, USA. Remote Sens. Environ., 150, 152162, https://doi.org/10.1016/j.rse.2014.04.023.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neigh, C. S. R., C. J. Tucker, and J. R. G. Townshend, 2008: North American vegetation dynamics observed with multi-resolution satellite data. Remote Sens. Environ., 112, 17491772, https://doi.org/10.1016/j.rse.2007.08.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nemani, R. R., C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, R. B. Myneni, and S. W. Running, 2003: Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300, 15601563, https://doi.org/10.1126/science.1082750.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Omernik, J. M., and G. E. Griffith, 2014: Ecoregions of the conterminous United States: Evolution of a hierarchical spatial framework. Environ. Manage., 54, 12491266, https://doi.org/10.1007/s00267-014-0364-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oswalt, S. N., and W. B. Smith, 2014: U.S. forest resource facts and historical trends. U.S. Department of Agriculture Rep. FS-1035, 64 pp., https://www.fia.fs.fed.us/library/brochures/docs/2012/ForestFacts_1952-2012_English.pdf.

    • Search Google Scholar
    • Export Citation
  • Pan, N. Q., X. M. Feng, B. J. Fu, S. Wang, F. Ji, and S. F. Pan, 2018: Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends. Remote Sens. Environ., 214, 5972, https://doi.org/10.1016/j.rse.2018.05.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pedelty, J., and Coauthors, 2007: Generating a long-term land data record from the AVHRR and MODIS instruments. 2007 IEEE Int. Geoscience and Remote Sensing Symp., Barcelona, Spain, Institute of Electrical and Electronics Engineers, 10211025, https://doi.org/10.1109/IGARSS.2007.4422974.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pedhazur, E. J., 1997: Multiple Regression in Behavioral Research: Exploration and Prediction. 4th ed. Harcourt Brace, 1072 pp.

  • Pinzon, J. E., and C. J. Tucker, 2014: A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens., 6, 69296960, https://doi.org/10.3390/rs6086929.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Privette, J. L., C. Fowler, G. A. Wick, D. Baldwin, and W. J. Emery, 1995: Effects of orbital drift on Advanced Very High Resolution Radiometer products: Normalized difference vegetation index and sea surface temperature. Remote Sens. Environ., 53, 164171, https://doi.org/10.1016/0034-4257(95)00083-D.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reed, B. C., J. F. Brown, D. Vanderzee, T. R. Loveland, J. W. Merchant, and D. O. Ohlen, 1994: Measuring phenological variability from satellite imagery. J. Veg. Sci., 5, 703714, https://doi.org/10.2307/3235884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reidmiller, D. R., C. W. Avery, D. R. Easterling, K. E. Kunkel, K. L. M. Lewis, T. K. Maycock, and B. C. Stewart, Eds., 2018: Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment. Vol. II, U.S. Global Change Research Program, 1515 pp., https://doi.org/10.7930/NCA4.2018.

    • Search Google Scholar
    • Export Citation
  • Sayler, K. L., W. Acevedo, and J. L. Taylor, Eds., 2016: Status and trends of land change in the eastern United States—1973 to 2000. U.S. Geological Survey Professional Paper 1794-D, 195 pp., https://doi.org/10.3133/pp1794D.

    • Search Google Scholar
    • Export Citation
  • Schut, A. G. T., E. Ivits, J. G. Conijn, B. ten Brink, and R. Fensholt, 2015: Trends in global vegetation activity and climatic drivers indicate a decoupled response to climate change. PLOS ONE, 10, e0138013, https://doi.org/10.1371/journal.pone.0138013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, M. D., and B. C. Reed, 1999: Surface phenology and satellite sensor-derived onset of greenness: An initial comparison. Int. J. Remote Sens., 20, 34513457, https://doi.org/10.1080/014311699211499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slayback, D. A., J. E. Pinzon, S. O. Los, and C. J. Tucker, 2003: Northern Hemisphere photosynthetic trends 1982–99. Global Change Biol., 9, 115, https://doi.org/10.1046/j.1365-2486.2003.00507.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, W. K., and Coauthors, 2019: Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities. Remote Sens. Environ., 233, 111401, https://doi.org/10.1016/j.rse.2019.111401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sobrino, J., and Y. Julien, 2016: Exploring the validity of the long-term data record V4 database for land surface monitoring. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 36073614, https://doi.org/10.1109/JSTARS.2016.2567642.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Swets, D. L., B. C. Reed, J. R. Rowland, and S. E. Marko, 1999: A weighted least-squares approach to temporal smoothing of NDVI. From Image to Information: 1999 ASPRS Annual Conf., Portland, OR, American Society for Photogrammetry and Remote Sensing, http://pubs.er.usgs.gov/publication/70201050.

    • Search Google Scholar
    • Export Citation
  • Tarpley, J. D., 1991: The NOAA Global Vegetation Index product—A review. Global Planet. Change, 4, 189194, https://doi.org/10.1016/0921-8181(91)90091-A.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, S. D., T. A. Nelson, N. C. Coops, M. A. Wulder, and T. C. Lantz, 2017: Global spatial–temporal variability in terrestrial productivity and phenology regimes between 2000 and 2012. Ann. Assoc. Amer. Geogr., 107, 15191537, https://doi.org/10.1080/24694452.2017.1309964.

    • Search Google Scholar
    • Export Citation
  • Torstenson, W. L. F., J. C. Mosley, T. K. Brewer, M. W. Tess, and J. E. Knight, 2006: Elk, mule deer, and cattle foraging relationships on foothill and mountain rangeland. Rangeland Ecol. Manage., 59, 8087, https://doi.org/10.2111/05-001R1.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trishchenko, A. P., J. Cihlar, and Z. Q. Li, 2002: Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors. Remote Sens. Environ., 81, 118, https://doi.org/10.1016/S0034-4257(01)00328-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tucker, C. J., 1979: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ., 8, 127150, https://doi.org/10.1016/0034-4257(79)90013-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tucker, C. J., J. E. Pinzon, M. E. Brown, D. A. Slayback, E. W. Pak, R. Mahoney, E. F. Vermote, and N. El Saleous, 2005: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens., 26, 44854498, https://doi.org/10.1080/01431160500168686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • U.S. Geological Survey, 2018: USGS EROS Archive—Advanced Very High Resolution Radiometer—AVHRR. Earth Resources Observation and Science Center, accessed 9 October 2018, https://doi.org/10.5066/F7K35S5K.

    • Search Google Scholar
    • Export Citation
  • van Leeuwen, W. J. D., and Coauthors, 2010: Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel. Int. J. Wildland Fire, 19, 7593, https://doi.org/10.1071/WF08078.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Verbesselt, J., R. Hyndman, A. Zeileis, and D. Culvenor, 2010: Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sens. Environ., 114, 29702980, https://doi.org/10.1016/j.rse.2010.08.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Viña, A., A. A. Gitelson, A. L. Nguy-Robertson, and Y. Peng, 2011: Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ., 115, 34683478, https://doi.org/10.1016/j.rse.2011.08.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vogelmann, J. E., A. L. Gallant, H. Shi, and Z. Zhu, 2016: Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data. Remote Sens. Environ., 185, 258270, https://doi.org/10.1016/j.rse.2016.02.060.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walker, J. J., K. M. de Beurs, and R. H. Wynne, 2014: Dryland vegetation phenology across an elevation gradient in Arizona, USA, investigated with fused MODIS and Landsat data. Remote Sens. Environ., 144, 8597, https://doi.org/10.1016/j.rse.2014.01.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X. H., S. L. Piao, X. T. Xu, P. Ciais, N. MacBean, R. B. Myneni, and L. Li, 2015: Has the advancing onset of spring vegetation green-up slowed down or changed abruptly over the last three decades? Global Ecol. Biogeogr., 24, 621631, https://doi.org/10.1111/geb.12289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wessels, K. J., S. D. Prince, P. E. Frost, and D. van Zyl, 2004: Assessing the effects of human-induced land degradation in the former homelands of northern South Africa with a 1 km AVHRR NDVI time-series. Remote Sens. Environ., 91, 4767, https://doi.org/10.1016/j.rse.2004.02.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, J. C., M. A. Wulder, T. Hermosilla, N. C. Coops, and G. W. Hobart, 2017: A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens. Environ., 194, 303321, https://doi.org/10.1016/j.rse.2017.03.035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Williams, A. P., and Coauthors, 2013: Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Climate Change, 3, 292297, https://doi.org/10.1038/nclimate1693.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, X., K. Naegeli, and S. Wunderle, 2020: Geometric accuracy assessment of coarse-resolution satellite datasets: A study based on AVHRR GAC data at the sub-pixel level. Earth Syst. Sci. Data, 12, 539553, https://doi.org/10.5194/essd-12-539-2020.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wylie, B. K., L. Zhang, N. Bliss, L. Ji, L. L. Tieszen, and W. M. Jolly, 2008: Integrating modelling and remote sensing to identify ecosystem performance anomalies in the boreal forest, Yukon River basin, Alaska. Int. J. Digit. Earth, 1, 196220, https://doi.org/10.1080/17538940802038366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, C. Y., H. Y. Liu, A. P. Williams, Y. Yin, and X. C. Wu, 2016: Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Global Change Biol., 22, 28522860, https://doi.org/10.1111/gcb.13224.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, L. M., B. K. Wylie, L. L. Tieszen, and B. C. Reed, 1998: An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains. Remote Sens. Environ., 65, 2537, https://doi.org/10.1016/S0034-4257(98)00012-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., B. Wylie, T. Loveland, E. Fosnight, L. L. Tieszen, L. Ji, and T. Gilmanov, 2007: Evaluation and comparison of gross primary production estimates for the northern Great Plains grasslands. Remote Sens. Environ., 106, 173189, https://doi.org/10.1016/j.rse.2006.08.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, L., A. G. Dai, and B. Dong, 2018: Changes in global vegetation activity and its driving factors during 1982–2013. Agric. For. Meteor., 249, 198209, https://doi.org/10.1016/j.agrformet.2017.11.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Z. C., and Coauthors, 2016: Greening of the Earth and its drivers. Nat. Climate Change, 6, 791795, https://doi.org/10.1038/nclimate3004.

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