Skip to main content
SpringerLink
Log in

Vegetation productivity responses to drought on tribal lands in the four corners region of the Southwest USA

  • Research Article
  • Published:
Frontiers of Earth Science Aims and scope Submit manuscript

Abstract

For more than a decade, the Four Corners Region has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources while exacerbating soil erosion. These persistent droughts threaten ecosystem services, agriculture, and livestock activities, and expose the hypersensitivity of this region to inter-annual climate variability and change. Much of the intermountainWestern United States has sparse climate and vegetation monitoring stations, making fine-scale drought assessments difficult. Remote sensing data offers the opportunity to assess the impacts of the recent droughts on vegetation productivity across these areas. Here, we propose a drought assessment approach that integrates climate and topographical data with remote sensing vegetation index time series. Multisensor Normalized Difference Vegetation Index (NDVI) time series data from 1989 to 2010 at 5.6 km were analyzed to characterize the vegetation productivity changes and responses to the ongoing drought. A multi-linear regression was applied to metrics of vegetation productivity derived from the NDVI time series to detect vegetation productivity, an ecosystem service proxy, and changes. The results show that around 60.13% of the study area is observing a general decline of greenness (p<0.05), while 3.87% show an unexpected green up, with the remaining areas showing no consistent change. Vegetation in the area show a significant positive correlation with elevation and precipitation gradients. These results, while, confirming the region’s vegetation decline due to drought, shed further light on the future directions and challenges to the region’s already stressed ecosystems. Whereas the results provide additional insights into this isolated and vulnerable region, the drought assessment approach used in this study may be adapted for application in other regions where surface-based climate and vegetation monitoring record is spatially and temporally limited.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Alcaraz D, Paruelo J, Cabello J (2006). Identification of current ecosystem functional types in the Iberian Peninsula. Glob Ecol Biogeogr, 15(2): 200–212

    Article  Google Scholar 

  • Anyamba A, Tucker C (2005). Analysis of sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981–2003. J Arid Environ, 63(3): 596–614

    Article  Google Scholar 

  • Bainbridge D A (2012). Restoration of arid and semi-arid lands. Restoration Ecology: The New Frontier, 115

    Chapter  Google Scholar 

  • Below R, Grover-Kopec E, Dilley M (2007). Documenting droughtrelated disasters: a global reassessment. J Environ Dev, 16(3): 328–344

    Article  Google Scholar 

  • Boschetti M, Nutini F, Brivio P A, Bartholomé E, Stroppiana D, Hoscilo A (2013). Identification of environmental anomaly hot spots in West Africa from time series of NDVI and rainfall. ISPRS Journal of Photogrammetry and Remote Sensing, 78: 26–40

    Article  Google Scholar 

  • Breshears D D, Cobb N S, Rich P M, Price K P, Allen C D, Balice R G, Romme WH, Kastens J H, Floyd ML, Belnap J, Anderson J J, Myers O B, Meyer C W (2005). Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci USA, 102(42): 15144–15148

    Article  Google Scholar 

  • Byun H, Wilhite D A (1999). Objective quantification of drought severity and duration. J Clim, 12(9): 2747–2756

    Article  Google Scholar 

  • Cai X L, Sharma B R (2010). Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the indo-gangetic river basin. Agric Water Manage, 97(2): 309–316

    Article  Google Scholar 

  • Cook E R, Woodhouse C A, Eakin CM, Meko DM, Stahle DW (2004). Long-term aridity changes in the western United States. Science, 306(5698): 1015–1018

    Article  Google Scholar 

  • Crimmins M A, Selover N, Cozzetto K, Chief K (2013). Technical Review of the Navajo Nation Drought Contingency Plan–Drought Monitoring. Meadow A M, ed. Tucson, AZ: Climate Assessment for the Southwest

  • Delbart N, Le Toan T, Kergoat L, Fedotova V (2006). Remote sensing of spring phenology in boreal regions: a free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982–2004). Remote Sens Environ, 101(1): 52–62

    Article  Google Scholar 

  • Di Luzio M, Johnson G L, Daly C, Eischeid J K, Arnold J G (2008). Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. J Appl Meteorol Climatol, 47(2): 475–497

    Article  Google Scholar 

  • Didan K (2010). Multi-satellite earth science data record for studying global vegetation trends and changes. In: Proceedings of the 2010 international geoscience and remote sensing symposium, Honolulu, HI, USA, (Vol. 2530, p. 2530)

    Google Scholar 

  • Didan K, Barreto A M, Miura T, Tsend-Ayush J, Zhang X, Friedl M, Gray J, Van Leeuwen W, Czapla-Myers J, Doman B S, Jenkerson C, Maiersperger T, Meyer D (2016). Multi-Sensor Vegetation Index and Phenology Earth Science Data Records: Algorithm Theoretical Basis Document and User Guide Version 4.0 (https://vip.arizona.edu/VIP_ATBD_UsersGuide.php)

    Google Scholar 

  • Fang J, Piao S, Tang Z, Peng C, Ji W (2001). Interannual variability in net primary production and precipitation. Science, 293(5536): 1723

    Article  Google Scholar 

  • Fensholt R, Langanke T, Rasmussen K, Reenberg A, Prince S D, Tucker C, Scholes R J, Le Q B, Bondeau A, Eastman R, Epstein H, Gaughan A E, Hellden U, Mbow C, Olsson L, Paruelo J, Schweitzer C, Seaquist J, Wessels K (2012). Greenness in semi-arid areas across the globe 1981–2007—An earth observing satellite based analysis of trends and drivers. Remote Sens Environ, 121: 144–158

    Article  Google Scholar 

  • Ferguson D, Crimmins M A (2009). Who’s paying attention to the drought on the Colorado Plateau. Southwest Climate Outlook, 3–6. http://www.climas.arizona.edu/sites/default/files/pdf2009juldroughtcoplateau.pdf

    Google Scholar 

  • Gamon J A, Huemmrich K F, Stone R S, Tweedie C E (2013). Spatial and temporal variation in primary productivity (NDVI) of coastal alaskan tundra: decreased vegetation growth following earlier snowmelt. Remote Sens Environ, 129: 144–153

    Article  Google Scholar 

  • Garfin G, Ellis A, Selover N, Anderson D, Tecle A, Heinrich P, Crimmins M, Leeper J, Tallsalt-Robertson J, Harvey C (2007). Assessment of the Navajo Nation Hydroclimate Network: A Final Report–12/28/2007. Navajo Nation Department of Water Resources. Available on the web: http://www.azwaterinstitute.org/media/Garfin%20fact%20sheet

    Google Scholar 

  • Gesch D B, Oimoen M J, Zhang Z, Meyer D J, Danielson J J (2012). Validation of the ASTER Global Digital Elevation Model Version 2 over the conterminous United States. In Imaging a sustainable future, 22nd Congress, 281–286

    Google Scholar 

  • Grahame J D, Sisk T D (2002). Canyons, cultures and environmental change: an introduction to the land-use history of the Colorado Plateau. The Land Use History of North America Program, United States Geological Survey

    Google Scholar 

  • Gray S T, Betancourt J L, Fastie C L, Jackson S T (2003). Patterns and sources of multidecadal oscillations in drought-sensitive tree-ring records from the central and southern Rocky Mountains. Geophys Res Lett, 30(6), doi: 10.1029/2002GL016154

    Google Scholar 

  • Griffin D, Woodhouse C A, Meko D M, Stahle D W, Faulstich H L, Carrillo C, Touchan R, Castro C L, Leavitt S W (2013). North American monsoon precipitation reconstructed from tree-ring latewood. Geophys Res Lett, 40(5): 954–958

    Article  Google Scholar 

  • Herrmann S M, Didan K, Barreto-Munoz A, Crimmins M A (2016). Divergent responses of vegetation cover in Southwestern US ecosystems to dry and wet years at different elevations. Environ Res Lett, 11(12): 124005

    Article  Google Scholar 

  • Horion S, Cornet Y, Erpicum M, Tychon B (2012). Studying interactions between climate variability and vegetation dynamic using a phenology based approach. Int J Appl Earth Obs Geoinf, 20(1): 20–32

    Google Scholar 

  • Huete A R, Restrepo-Coupe N, Ratana P, Didan K, Saleska S R, Ichii K, Panuthai S, Gamo M (2008). Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia. Agricultural and Forest Meteorology, 148(5): 748–760

    Article  Google Scholar 

  • Jolly W M, Dobbertin M, Zimmermann N E, Reichstein M (2005). Divergent vegetation growth responses to the 2003 heat wave in the Swiss Alps. Geophys Res Lett, 32(18), doi: 10.1029/2005GL023252

    Google Scholar 

  • Kaplan S (2012). Response of urban and non-urban land cover in a semiarid ecosystem to summer precipitation variability. J Ariz Nev Acad Sci, 43(2): 77–85

    Article  Google Scholar 

  • Karnieli A (2003). Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments. Int J Biometeorol, 47(4): 179–187

    Article  Google Scholar 

  • Keshavarz M, Karami E, Vanclay F (2013). The social experience of drought in rural iran. Land Use Policy, 30(1): 120–129

    Article  Google Scholar 

  • Liang T, Feng Q, Yu H, Huang X, Lin H, An S, Ren J (2012). Dynamics of natural vegetation on the Tibetan Plateau from past to future using a comprehensive and sequential classification system and remote sensing data. Grassland science, 58(4): 208–220

    Article  Google Scholar 

  • Liu S, Gong P (2012). Change of surface cover greenness in China between 2000 and 2010. Chin Sci Bull, 57(22): 2835–2845

    Article  Google Scholar 

  • Ma M, Frank V (2006). Interannual variability of vegetation cover in the chinese heihe river basin and its relation to meteorological parameters. Int J Remote Sens, 27(16): 3473–3486

    Article  Google Scholar 

  • Mu Q, Zhao M, Kimball J S, McDowell N G, Running S W (2013). A remotely sensed global terrestrial drought severity index. Bulletin of the American Meteorological Society, 94(1): 83–98

    Article  Google Scholar 

  • NALCMS (2005). North American Land Cover at 250 m spatial resolution. Produced by Natural Resources Canada/Canadian Center for Remote Sensing (NRCan/CCRS), United States Geological Survey (USGS); Insituto Nacional de Estadística y Geografía (INEGI), Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO) and Comisión Nacional Forestal CONAFOR). https://landcover.usgs.gov/nalcms.php

    Google Scholar 

  • NCAR (2005). The US National Center for Atmospheric Research (NCAR) and the University Corporation for Atmospheric Research (UCAR);“drought’s growing reach: national center for atmospheric research study points to global warming as key factor”http://www.ucar.edu/news/releases/2005/drought_research.shtml

    Google Scholar 

  • Nemani R R, Keeling C D, Hashimoto H, Jolly W M, Piper S C, Tucker C J, Myneni R B, Running S W (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625): 1560–1563

    Article  Google Scholar 

  • Nicholson S E, Farrar T J (1994). The influence of soil type on the relationships between NDVI, precipitation, and soil moisture in semiarid Botswana. I. NDVI response to precipitation. Remote Sens Environ, 50(2): 107–120

    Article  Google Scholar 

  • Nieto S, Flombaum P, Garbulsky M F (2015). Can temporal and spatial NDVI predict regional bird-species richness? Global Ecology and Conservation, 3: 729–735

    Article  Google Scholar 

  • Obasi G O P (1994). WMO’s role in the international decade for natural disaster reduction. Bull Am Meteorol Soc, 75(9): 1655–1661

    Article  Google Scholar 

  • Ouyang W, Hao F, Skidmore A K, Groen T A, Toxopeus A G, Wang T (2012). Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed. Ecol Indic, 18: 163–170

    Article  Google Scholar 

  • Palmer W C (1968). Keeping track of crop moisture conditions, nationwide: the new crop moisture index. Weatherwise, 21(4): 156–161

    Article  Google Scholar 

  • Pape MS, Peterson A T, Powell G V N (2012). Vegetation dynamics and avian seasonal migration: clues from remotely sensed vegetation indices and ecological niche modelling. J Biogeogr, 39(4): 652–664

    Article  Google Scholar 

  • Peng Y, Gitelson A A, Sakamoto T (2013). Remote estimation of gross primary productivity in crops using MODIS 250 m data. Remote Sens Environ, 128: 186–196

    Article  Google Scholar 

  • Pôças I, Cunha M, Pereira L S, Allen R G (2013). Using remote sensing energy balance and evapotranspiration to characterize montane landscape vegetation with focus on grass and pasture lands. Int J Appl Earth Obs Geoinf, 21: 159–172

    Article  Google Scholar 

  • Reynolds J F, Stafford S D M, Olsson L (2003). Geographical reviewsglobal desertification: Do humans cause deserts? Geogr Rev, 93(3): 413

    Google Scholar 

  • Ryu Y, Baldocchi D D, Verfaillie J, Ma S, Falk M, Ruiz-Mercado I, Hehn T, Sonnentag O (2010). Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function. Agric Meteorol, 150(12): 1597–1606

    Article  Google Scholar 

  • Shafer B A, Dezman L E (1982). Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. In: Proceedings of the western snow conference. Vol. 50. Fort Collins, CO: Colorado State University

    Google Scholar 

  • Shi J, Jackson T, Tao J, Du J, Bindlish R, Lu L, Chen K S (2008). Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sens Environ, 112(12): 4285–4300

    Article  Google Scholar 

  • Sivakumar M, Motha R, Wilhite D, Wood D (2010). Agricultural Drought Indices Proceedings of An Expert Meeting 2–4 June 2010, Murcia, Spain. Geneva: World Meteorological Organization, 219

    Google Scholar 

  • UNDP/UNSO (1997). Aridity zones and dryland populations: an assessment of population levels in the world’s drylands. New York: Office to Combat Desertification and Drought

    Google Scholar 

  • UNESCO (2012). World water development report managing water under uncertainty and risk. The United Nations world water development report 4. World water assessment programme. http:// www.unesco.org/new/en/natural-sciences/environment/water/wwap/ wwdr/wwdr4-2012/

    Google Scholar 

  • Wang J, Rich P M, Price K P (2003). Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int J Remote Sens, 24(11): 2345–2364

    Article  Google Scholar 

  • Wang Y (2012). Detecting vegetation recovery patterns after hurricanes in south florida using NDVI time series. Open Access Theses. Paper 355

    Google Scholar 

  • Weiss J, Gutzler D S, Coonrod J E A, Dahm C N (2004). Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico, USA. J Arid Environ, 58(2): 249–272

    Article  Google Scholar 

  • Wessels K J, Prince S D, Malherbe J, Small J, Frost P, Van Zyl D (2007). Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa. J Arid Environ, 68(2): 271–297

    Article  Google Scholar 

  • White M A, Nemani R R (2006). Real-time monitoring and short-term forecasting of land surface phenology. Remote Sens Environ, 104(1): 43–49

    Article  Google Scholar 

  • Wright C K, de Beurs K M, Henebry G M (2012). Combined analysis of land cover change and NDVI trends in the Northern Eurasian grain belt. Front Earth Sci, 6(2): 177–187

    Article  Google Scholar 

  • Yin H, Udelhoven T, Fensholt R, Pflugmacher D, Hostert P (2012). How normalized difference vegetation index (NDVI) trendsfrom advanced very high resolution radiometer (AVHRR) and système probatoire d’observation de la terre vegetation (SPOT VGT) time series differ in agricultural areas: An inner Mongolian case study. Remote Sens, 4(11): 3364–3389

    Article  Google Scholar 

  • Yuan F, Roy S S (2007). Analysis of the relationship between NDVI and climate variables in minnesota using geographically weighted regression and spatial interpolation. In American Society for Photogrammetry and Remote Sensing- ASPRS Annual Conference 2007: Identifying Geospatial Solutions, 2: 784–789

    Google Scholar 

  • Zhang X, Friedl MA, Schaaf C B, Strahler A H (2004). Climate controls on vegetation phenological patterns in northern mid-and high latitudes inferred from MODIS data. Glob Change Biol, 10(7): 1133–1145

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by NASA (Grant No. NNX11AG56G) and NASA MEaSUREs (Grant No. NNX08AT05A) (Kamel Didan, PI) and the NOAA Sectoral Applications Research Program (NA10OAR4310183) (Michael Crimmins, PI). We also thank the three anonymous reviewers and the editor for their valuable and constructive comments.

Author information

Authors and Affiliations

  1. The International Food Policy Research Institute, Washington, DC, 20006-1002, USA

    Mohamed Abd Salam El-Vilaly

  2. Vegetation Index and Phenology Lab, Department of Agricultural and Biosystems Engineering, The University of Arizona, Tucson, AZ, 85721-0036, USA

    Kamel Didan & Armando Barreto Munoz

  3. Arizona Remote Sensing Center, School of Natural Resources and the Environment, The University of Arizona, Tucson, AZ, 85721-0043, USA

    Stuart E. Marsh & Willem J. D. van Leeuwen

  4. Department of Soils Water and Environmental Science, The University of Arizona, Tucson, AZ, 85721-0038, USA

    Michael A. Crimmins

Authors
  1. Mohamed Abd Salam El-Vilaly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamel Didan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stuart E. Marsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Willem J. D. van Leeuwen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael A. Crimmins
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Armando Barreto Munoz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Abd Salam El-Vilaly.

Additional information

Mohamed Abd Salam El-Vilaly received a B.Sc. in water resources management from the University of Nouakchott, Mauritania in 2001, M.Sc. degree in hydrology from the University of Avignon, France in 2004, and a Ph.D. degrees in Arid Lands Resource Sciences from the University of Arizona, Tucson in 2013. Dr. Abd-Salam is a Research Fellow at the International Food Policy Research Institute (IFPRI), Washington, DC, USA. He has worked over 16 years in the humanitarian, academic, and private sectors. His major areas of specialization include hydrology, groundwater extraction, and the use of Geographic Information Science (GIS) & Remote Sensing for monitoring ecological change at a global scale. Specifically, Abd Salam’s work focuses on human-environment interactions related to resource access, natural hazards, and human and animal health.

Kamel Didan received the B.Sc. from the National Institute of Agricultural Engineering, Tunis, Tunisia, in 1988, M.Sc. and Ph. D. in 1991 and 1999 respectively, from the Department of Agricultural and Biosystems Engineering (ABE), University of Arizona, Tucson. He is currently an Associate Professor in the ABE Department, at the University of Arizona, Tucson, where he directs the Vegetation Index and Phenology Laboratory that specializes in long term multi-sensor global remote sensing and the development of algorithms for generating time series data in support of global change studies. This work intersects the concepts of big data analytics, precision agriculture and mapping, and the development of online big data tools. Prof. Didan is a member of the American Geophysical Union and IEEE Geosciences and Remote Sensing Society. He received the NASA Certificate of Recognition for Services and Contribution to the EOS program and serves on multiple NASA Earth science working groups.

Stuart E. Marsh received the B.S. degree (1973) in Geology from George Washington University, Washington, DC, and the M.S. (1975) and Ph.D. (1979) degrees in applied earth sciences from Stanford University, Stanford, California. Professor Marsh joined the faculty at the University of Arizona in 1988. Prior to coming to the UA he held positions in industry and the federal government. Dr. Stuart is Director of the School of Natural Resources and the Environment at the University of Arizona. He served as Chair of the Arid Lands Resource Sciences Interdisciplinary Ph.D. Program for 10 years (2002‒2012) and on the Graduate Interdisciplinary Programs Advisory Committee (GIDPAC) from 2007 through 2010. He also served as Director of the Arizona Remote Sensing Center from 2004 through 2011. Dr. Marsh was a recipient of a J. William Fulbright Senior Scholar Award, multiple awards from the American Society of Photogrammetry and Remote Sensing, NASA, and the USDA. His research focuses on the integration and analysis of multitemporal airborne and satellite remote sensing data with GIS technologies to map and monitor environmental change. He has worked on mapping land-cover change in environmentally sensitive areas of Africa, the Middle East, Mexico, and the U.S. This work helped to create new techniques to map land-cover change at global scales, develop rule-based and geostatistical models of vegetation distribution under varying climatic regimes, and evaluate the environmental impacts of landcover change within arid environments and urban riparian and rural/urban fringe habitats.

Willem J.D. van Leeuwen received the B. Sc. and M.Sc. degrees in Soil Science from the Wageningen University for Life Sciences, the Netherlands in 1985 and 1987 respectively, and a Ph.D. from the Department of Soil, Water and Environmental Science, University of Arizona, Tucson in 1995. Dr. Willem is an Associate Professor in the School of Natural Resources and the Environment & the School of Geography and Development at the University of Arizona, Tucson. He has more than 20 years of remote sensing science experience and is the director of the Arizona Remote Sensing Center at the University of Arizona. His research interests focus on multi-platform remote sensing data fusion and product development for resource monitoring, management and decision making. Dr. van Leeuwen is a member of the American Geophysical Union, Association of American Geographers and Ecological Society of America.

Michael A. Crimmins received a B.Sc. in Atmospheric Science from the University of Michigan in 1996, a M.Sc. in Geography from Western Michigan University in 1998 and Ph.D. in Geography with a focus on climatology from the University of Arizona in 2004. Dr. Crimmins is an Associate Professor and Extension Specialist in the Department of Soil, Water, and Environmental Sciences and has been in this position since 2005. His research expertise focuses on applied climate research in arid regions including the southwest U.S. studying climate extremes from droughts to floods. As an Extension Specialist he works with resource managers, agricultural producers, and decision makers to use climate information and applied climate research to support decision making, planning and management. Dr. Crimmins is a member of the American Geophysical Union and American Meteorological Society.

Armando Barreto Munoz received a M.Sc. and Ph.D. degrees in Agriculture and Biosystems Engineering at the University of Arizona, Tucson AZ USA, in 2006 and 2013 respectively. Dr. Barreto has more than 8 years of remote sensing science experience developing algorithms for spatial data processing and creating online applications at the Vegetation Index and Phenology Laboratory at the University of Arizona. His research interest focus on long term data records from multiple platforms, data quality analysis, and production of vegetation indices and phenology datasets. Dr. Barreto is a member of the American Geophysical Union and NASA MODIS/VIIRS land science team.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El-Vilaly, M.A.S., Didan, K., Marsh, S.E. et al. Vegetation productivity responses to drought on tribal lands in the four corners region of the Southwest USA. Front. Earth Sci. 12, 37–51 (2018). https://doi.org/10.1007/s11707-017-0646-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11707-017-0646-z

Keywords

Search

Navigation