Quantifying drought's influence on moist soil seed vegetation in California's Central Valley through remote sensing.
Central Valley
drought
machine learning
managed wetland
moist soil management
remote sensing
waterfowl food resources
Journal
Ecological applications : a publication of the Ecological Society of America
ISSN: 1051-0761
Titre abrégé: Ecol Appl
Pays: United States
ID NLM: 9889808
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
04
10
2019
revised:
20
02
2020
accepted:
30
03
2020
pubmed:
30
4
2020
medline:
22
1
2021
entrez:
30
4
2020
Statut:
ppublish
Résumé
California's Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 2007 to 2017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley, we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n = 68, R
Substances chimiques
Soil
0
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e02153Subventions
Organisme : U.S. Geological Survey
Pays : International
Organisme : NASA
ID : NNX17AG81G
Pays : United States
Informations de copyright
© 2020 by the Ecological Society of America.
Références
Bedsworth, L., D. Cayan, G. Franco, L. Fisher, S. Ziaja, and California Governor’s Office of Planning and Research, Scripps Institution of Oceanography, California Energy Commission, and California Public Utilities Commission. 2018. Statewide summary report. California’s Fourth Climate Change Assessment. Publication number: SUMCCCA4-2018-013. https://www.energy.ca.gov/sites/default/files/2019-07/Statewide%20Reports-%20SUM-CCCA4-2018-013%20Statewide%20Summary%20Report.pdf
Ben-Hur, A., and J. Weston. 2010. A user's guide to support vector machines. Pages 223-239inO. Carungo, and Eisenhaber, F. editors. Data mining techniques for the life sciences. Humana Press, Totowa, New Jersey, USA.
Berg, N., and A. Hall. 2015. Increased interannual precipitation extremes over California under climate change. Journal of Climate 28:6324-6334.
Butcher, G., C. Barnes, and L. Owen. 2019. Landsat: The cornerstone of global land imaging. GIM International 2019:31-35.
Byrd, K. B., L. Ballanti, N. Thomas, D. Nguyen, J. R. Holmquist, M. Simard, and L. Windham-Myers. 2018. A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States. ISPRS Journal of Photogrammetry and Remote Sensing 139:255-271.
Byrd, K. B., and A. A. Lorenz. 2018. Wetland moist soil seed extent and productivity maps for the Central Valley of California 2007-2017: U.S. Geological Survey data release. https://doi.org/10.5066/P9PMO9Q2
Byrd, K. B., L. Windham-Myers, T. Leeuw, B. Downing, J. T. Morris, and M. C. Ferner. 2016. Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model. Ecosphere 7:e01582.
Cai, Y., K. Guan, J. Peng, S. Wang, C. Seifert, B. Wardlow, and Z. Li. 2018. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment 210:35-47.
California State University Chico, Department of Geography and Planning, and Geographic Information Center. 2003. The Central Valley historic mapping project. https://www.waterboards.ca.gov/waterrights/water_issues/programs/bay_delta/docs/cmnt081712/sldmwa/csuchicodptofgeographyandplanningcentralvalley.pdf
Central Valley Joint Venture (CVJV). 2006. Central Valley Joint Venture Implementation Plan-conserving bird habitat. U.S. Fish and Wildlife Service, Sacramento, California, USA. http://www.centralvalleyjointventure.org/assets/pdf/CVJV_fnl.pdf
Checkett, J. M., R. D. Drobney, M. J. Petrie, and D. A. Graber. 2002. True metabolizable energy of moist-soil seeds. Wildlife Society Bulletin 30:1113-1119. https://www.jstor.org/stable/3784280
Congalton, R. G., and K. Green 2009. Assessing the accuracy of remotely sensed data, principles and practices. Second edition. CRC Press, Taylor and Francis Group, Baca Raton, Florida, USA.
Dahl, T. E. 1990. Wetland losses in the United States 1780s to 1980s. Department of the Interior, Fish and Wildlife Service, Washington, D.C., USA. https://www.fws.gov/wetlands/documents/Wetlands-Losses-in-the-United-States-1780s-to-1980s.pdf
Dahl, T. E. 2006. Status and trends of wetlands in the conterminous United States 1998 to 2004. U.S. Department of the Interior; Fish and Wildlife Service, Washington, D.C., USA. https://www.fws.gov/wetlands/documents/Status-and-Trends-of-Wetlands-in-the-Conterminous-United-States-1998-to-2004.pdf
Diffenbaugh, N. S., D. L. Swain, and D. Touma. 2015. Anthropogenic warming has increased drought risk in California. Proceedings of the National Academy of Sciences 112:3931-3936. https://doi.org/10.1073/pnas.1422385112
Dwyer, J. L., D. P. Roy, B. Sauer, C. B. Jenkerson, H. K. Zhang, and L. Lymburner. 2018. Analysis ready data: enabling analysis of the Landsat archive. Remote Sensing 10:1363.
Dybala, K. E., M. E. Reiter, C. M. Hickey, W. D. Shuford, K. M. Strum, and G. S. Yarris. 2017. A bioenergetics approach to setting conservation objectives for non-breeding shorebirds in California’s Central Valley. San Francisco Estuary and Watershed Science 15. https://escholarship.org/uc/item/1pd2q7sx
Fleskes, J. P. 2012. Wetlands of the Central Valley of California and Klamath Basin. Pages 357-370 in D. Batzer and A. Baldwin, editors. Wetland habitats of North America: ecology and conservation concerns. University of California Press, Berkeley, California, USA.
Frayer, W. E., D. D. Peters, and H. R. Pywell 1989. Wetlands of the California Central Valley: status and trends-1939 to mid-1980s. U.S. Department of the Interior, Fish and Wildlife Service, Portland, Oregon, USA. https://www.fws.gov/wetlands/Documents/Wetlands-of-the-California-Central-Valley-Status-and-Trends-1939-to-mid-1980s.pdf
Fredrickson, L. H. 1996. Moist-soil management-thirty years of field experimentation. Pages 168-177inJ. T. Ratti editor. Proceedings of the Seventh International Waterfowl Symposium, Memphis, Tennessee, USA.
Fredrickson, L. H., and T. S. Taylor1982. Management of seasonally flooded impoundments for wildlife. U.S. Fish and Wildlife Service, Resource Publication 148,Washington, D.C., USA.https://apps.dtic.mil/dtic/tr/fulltext/u2/a323232.pdf
Garone, P. 2011. The fall and rise of the wetlands of California's great central valley. University of California Press, Berkeley, California, USA.
Gopal, S., and C. Woodcock. 1994. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogrammetric Engineering and Remote Sensing 60:181-188.
Gray, M. J., M. A. Foster, and L. A. P. Peniche. 2009. New technology for estimating seed production of moist-soil plants. Journal of Wildlife Management 73:1229-1232.
GreenInfo Network. 2017. California protected areas database. https://www.calands.org/cpad/
Griffin, D., and K. J. Anchukaitis. 2014. How unusual is the 2012-2014 California drought? Geophysical Research Letters 41:9017-9023.
Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25:295-309.
Kross, J., R. M. Kaminski, K. J. Reinecke, E. J. Penny, and A. T. Pearse. 2008. Moist-soil seed abundance in managed wetlands in the Mississippi Alluvial Valley. Journal of Wildlife Management 72:707-714.
Kuhn, M. 2008. Building predictive models in R using the caret package. Journal of Statistical Software 28:26.
Lane, J. J., and K. C. Jensen1999. Moist-soil impoundments for wetland wildlife. Technical Report EL-99-11. U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, USA.https://www.fwspubs.org/doi/suppl/10.3996/072013-JFWM-050/suppl_file/072013-jfwm-050.s5.pdf
Laubhan, M. K., and L. H. Fredrickson. 1992. Estimating seed production of common plants in seasonally flooded wetlands. Journal of Wildlife Management 56:329-337.
Lumley, T.2017. leaps: Regression Subset Selection. R package version 3.0. Based on Fortran code by Alan Miller. https://CRAN.R-project.org/package=leaps
Major, J. 1977. California climate in relation to vegetation. Pages 11-74 in M. G. Barbour, and J. Major, editors. Terrestrial vegetation of California. Wiley, New York, New York, USA.
Mann, M. E., and P. H. Gleick. 2015. Climate change and California drought in the 21st century. Proceedings of the National Academy of Sciences 112:3858-3859. https://doi.org/10.1073/pnas.1503667112
Marshall, M., and P. Thenkabail. 2015. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS Journal of Photogrammetry and Remote Sensing 108:205-218.
Matchett, E. L., and J. P. Fleskes. 2017. Projected Impacts of Climate, Urbanization, Water Management, and Wetland Restoration on Waterbird Habitat in California’s Central Valley. PLoS ONE 12:e0169780.
Mensik, J. G., and P. O. O'Halloran. 1990. Monitoring marsh management on the Sacramento National Wildlife Refuge Complex. Transactions of the Western Section of the Wildlife Society 26:24-28.
Merenlender, A., M. Deitch, and S. Feirer. 2008. Decision support tool seeks to aid stream-flow recovery and enhance water security. California Agriculture 62:148-155.
Mo, Y., B. Momen, and M. S. Kearney. 2015. Quantifying moderate resolution remote sensing phenology of Louisiana coastal marshes. Ecological Modelling 312:191-199.
Moyle, P. B., A. D. Manfree, and P. L. Fiedler, editors. 2014. Suisun Marsh: Ecological history and possible futures. University of California Press, Berkeley, California, USA.
Naylor, L. W. 2002. Evaluating moist-soil seed production and management in Central Valley wetlands to determine habitat needs for waterfowl. Thesis. University of California, Davis, California, USA.
Naylor, L. W., J. M. Eadie, W. D. Smith, M. Eichholz, and M. J. Gray. 2005. A simple method to predict seed yield in moist-soil habitats. Wildlife Society Bulletin 33:1335-1341.
North American Waterfowl Management Plan. 2012. North American waterfowl management plan: people conserving waterfowl and wetlands. Canadian Wildlife Service, U.S. Fish and Wildlife Service, Secretaria de Medio Ambiente y Recursos Naturales.https://nawmp.org/sites/default/files/2017-12/NAWMP-Plan-EN-may23_0.pdf
Olofsson, P., G. M. Foody, M. Herold, S. V. Stehman, C. E. Woodcock, and M. A. Wulder. 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment 148:42-57.
Ortega, R. 2009. Wetland response to adaptive salinity drainage management. Thesis. University of California, Davis, California, USA.
Pasquarella, V. J., C. E. Holden, and C. E. Woodcock. 2018. Improved mapping of forest type using spectral-temporal Landsat features. Remote Sensing of Environment 210:193-207.
Petrie, M. J., J. P. Fleskes, M. A. Wolder, C. R. Isola, G. S. Yarris, and D. A. Skalos. 2016. Potential effects of drought on carrying capacity for wintering waterfowl in the Central Valley of California. Journal of Fish and Wildlife Management 7:408-422.
Petrik, K., D. Fehringer, and A. Weverko2014. Mapping seasonal managed and semi-permanent wetlands in the Central Valley of California. Rancho Cordova, California, USA.
Pettorelli, N., W. F. Laurance, T. G. O'Brien, M. Wegmann, H. Nagendra, and W. Turner. 2014. Satellite remote sensing for applied ecologists: opportunities and challenges. Journal of Applied Ecology 51:839-848.
Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, drought, and sea level rise scenarios for the fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.
Planet Team. 2017. Planet application program interface: In space for life on earth. San Francisco, California, USA. https://api.planet.com
Qi, J., A. Chehbouni, A. R. Huete, Y. H. Kerr, and S. Sorooshian. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment 48:119-126.
Quinn, G. P., and M. J. Keough 2002. Experimental design and data analysis for biologists. Cambridge University Press, Cambridge, UK.
R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Rahilly, P., D. Li, Q. Guo, J. Zhu, R. Ortega, N. W. T. Quinn, and T. C. Harmon. 2012. Mapping swamp timothy (Crypsis schoenoides) seed productivity using spectral values and vegetation indices in managed wetlands. International Journal of Remote Sensing 33:4902-4918.
Reid, F. A., J. R. Kelley, T. S. Taylor, and L. H. Fredrickson 1989. Upper Mississippi Valley wetlands-refuges and moist soil impoundments. Pages 181-202 in L. M. Smith, R. L. Pederson, and R. M. Kaminski, editors. Habitat management for migrating and wintering waterfowl in North America. Texas Tech University Press, Lubbock, Texas, USA.
Reiter, M. E., N. K. Elliott, D. Jongsomjit, G. H. Golet, and M. D. Reynolds. 2018. Impact of extreme drought and incentive programs on flooded agriculture and wetlands in California’s Central Valley. PeerJ 6:e5147. https://doi.org/10.7717/peerj.5147
Reiter, M. E., N. Elliott, S. Veloz, D. Jongsomjit, C. M. Hickey, M. Merrifield, and M. D. Reynolds. 2015. Spatio-temporal patterns of open surface water in the Central Valley of California 2000-2011: Drought, land cover, and waterbirds. JAWRA Journal of the American Water Resources Association 51:1722-1738.
Robeson, S. M. 2015. Revisiting the recent California drought as an extreme value. Geophysical Research Letters 42:6771-6779.
Rocchio, J. 2005. North American arid west freshwater marsh ecological system ecological integrity assessment. Colorado Natural Heritage Program, Fort Collins, Colorado, USA.
Schwenkler, J., and D. Hickson. 2016. Vegetation-Great Valley Ecoregion [ds2632]. California Department of Fish and Wildlife. https://map.dfg.ca.gov/bios/?al=ds2632
Shao, Y., and R. S. Lunetta. 2012. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS Journal of Photogrammetry and Remote Sensing 70:78-87.
Shuford, W. D., and K. E. Dybala. 2017. Conservation objectives for wintering and breeding waterbirds in California's Central Valley. San Francisco Estuary and Watershed Science 15. https://escholarship.org/uc/item/5tp5m718
Smith, W. D., G. L. Rollins, and R. L. Shinn 1995. A Guide to Wetland Habitat Management in the Central Valley. California Department of Fish and Game and California Waterfowl Association, Sacramento,California, USA.
Stafford, J. D., A. P. Yetter, C. S. Hine, R. V. Smith, and M. M. Horath. 2011. Seed abundance for waterfowl in wetlands managed by the Illinois Department of Natural Resources. Journal of Fish and Wildlife Management 2:3-11.
Swain, D. L., B. Langenbrunner, J. D. Neelin, and A. Hall. 2018. Increasing precipitation volatility in twenty-first-century California. Nature Climate Change 8:427-433.
Thenkabail, P. S., E. A. Enclona, M. S. Ashton, and B. Van Der Meer. 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications. Remote Sensing of Environment 91:354-376.
Thenkabail, P. S., R. B. Smith, and E. De Pauw. 2002. Evaluation of narrowband and broadband vegetation indices for determing optimal hyperspectral wavebands for agricultural crop characterization. Photogrammetric Engineering and Remote Sensing 68:607-621.
Thorne, J. H., R. M. Boynton, L. E. Flint, and A. L. Flint. 2015. The magnitude and spatial patterns of historical and future hydrologic change in California's watersheds. Ecosphere 6:art24.
Tucker, C. J. 1977. Asymptotic nature of grass canopy spectral reflectance. Applied Optics 16:1151-1156.
U.S. Fish & Wildlife Service (USFWS)2009. Sacramento, Delevan, Colusa, and Sutter National Wildlife refuges-Final Comprehensive Conservation Plan and Environmental Assessment. USFWS, Sacramento, California, USA.
U.S. Fish and Wildlife Service (USFWS). 2015. National Wetlands Inventory Data. USFWS, St. Petersburg, Florida, USA.
U.S. Fish and Wildlife Service (USFWS). 2017. Waterbird habitat quality assessment for Central Valley National Wildlife Refuges: 2017. Unpublished data.
U.S. Fish and Wildlife Service (USFWS). 2018. Sacramento National Wildlife Refuge complex managed wetland plant species composition monitoring: 2009, 2014, 2016. Unpublished data.
U.S. Fish and Wildlife Service. 1978. Concept plan for waterfowl wintering habitat preservation: Central Valley, California. U.S. Department of Interior, Fish and Wildlife Service, Portland, Oregon, USA. https://catalog.hathitrust.org/Record/102513770
USDA Farm Service Agency. 2017. National Agriculture Imagery Program (NAIP). https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/index
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 Sensing of Environment 115:3468-3478.
Wallace, C. S. A., P. Thenkabail, J. R. Rodriguez, and M. K. Brown. 2017. Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) to map planted versus fallowed croplands using MODIS data to assist in drought studies leading to water and food security assessments. GIScience & Remote Sensing 54:258-282.
Whitson, M. D. 2017. Use of moist-soil management techniques for wintering waterfowl in fallow rice fields on the upper Texas coast. Thesis. Texas Tech University, Lubbock, Texas, USA.
Wieczorek, M.2014. Area- and depth- weighted averages of selected SSURGO variables for the conterminous United States and District of Columbia. Report 866. U.S. Geological Survey, Reston, Virginia, USA. http://pubs.er.usgs.gov/publication/ds866
Wieczorek, M. E.2015. STATSGO2 area- and depth- weighted averages of selected STATSGO2 variables. Science Base-Catalog. https://www.sciencebase.gov/catalog/item/54b97608e4b043905e00fc9d
Wilson, T. S., B. M. Sleeter, and D. R. Cameron. 2016. Future land-use related water demand in California. Environmental Research Letters 11:054018.
Zheng, B., S. W. Myint, P. S. Thenkabail, and R. M. Aggarwal. 2015. A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. International Journal of Applied Earth Observation and Geoinformation 34:103-112.