全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Assessing Regional Climate and Local Landcover Impacts on Vegetation with Remote Sensing

DOI: 10.3390/rs5094347

Keywords: landcover, information theory, wavelet analysis, precipitation, NDVI, vegetation

Full-Text   Cite this paper   Add to My Lib

Abstract:

Landcover change alters not only the surface landscape but also regional carbon and water cycling. The objective of this study was to assess the potential impacts of landcover change across the Kansas River Basin (KRB) by comparing local microclimatic impacts and regional scale climate influences. This was done using a 25-year time series of Normalized Difference Vegetation Index (NDVI) and precipitation (PPT) data analyzed using multi-resolution information theory metrics. Results showed both entropy of PPT and NDVI varied along a pronounced PPT gradient. The scalewise relative entropy of NDVI was the most informative at the annual scale, while for PPT the scalewise relative entropy varied temporally and by landcover type. The relative entropy of NDVI and PPT as a function of landcover showed the most information at the 512-day scale for all landcover types, implying different landcover types had the same response across the entire KRB. This implies that land use decisions may dramatically alter the local time scales of responses to global climate change. Additionally, altering land cover (e.g., for biofuel production) may impact ecosystem functioning at local to regional scales and these impacts must be considered for accurately assessing future implications of climate change.

References

[1]  Knapp, A.K.; Smith, M.D. Variation among biomes in temporal dynamics of aboveground primary production. Science 2001, 291, 481–484.
[2]  Bonan, G.B.; Levis, S.; Kergoat, L.; Oleson, K.W. Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models. Glob. Biogeochem. Cy 2002, 16, 5–23.
[3]  Farrell, A.E.; Plevin, R.J.; Turner, B.T.; Jones, A.D.; O’Hare, M.; Kammen, D.M. Ethanol can contribute to energy and environmental goals. Science 2006, 311, 506–508.
[4]  Lauenroth, W.K.; Dodd, J.L. Response water and of native nitrogen grassland treatments legumes to water and nitrogen treatments. J. Range Manag 1979, 32, 292–294.
[5]  Sala, O.E.; Parton, W.J.; Joyce, L.A.; Lauenroth, W.K. Primary production of the central grassland region of the United States. Ecology 1988, 69, 40–45.
[6]  Woodward, F.I. Climate and Plant Distribution; Cambridge University Press: New York, NY, USA, 1987; p. 188.
[7]  Yang, L. 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 1998, 65, 25–37.
[8]  Lotsch, A.; Friedl, M.A.; Anderson, B.T. Coupled vegetation-precipitation variability observed from satellite and climate records. Geophys. Res. Lett 2003, 30, 8–11.
[9]  Brunsell, N.; Jones, A.; Jackson, T.L. Seasonal trends in air temperature and precipitation in IPCC AR4 GCM output for Kansas, USA: Evaluation and implications. Int. J. Climatol 2010, 30, 1178–1193.
[10]  Philippon, N. Characterization of the interannual and intraseasonal variability of west African vegetation between 1982 and 2002 by means of NOAA AVHRR NDVI data. J. Clim 2007, 11, 2078–1218.
[11]  Zhou, L.; Tucke, C.J.; Kaufman, R.K.; Slayback, D.; Shabanov, N.V.; Myneni, R.B. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res 2001, 106, 20,069–20,083.
[12]  Myneni, R.B.; Los, S.O. Potential gross primary productivity of terrestrial vegetation from 1982–1990. Geophys. Res. Lett 1995, 22, 2617–2620.
[13]  Notaro, M.; Liu, Z.; Willians, J.W. Observed vegetation-climate feedbacks in the United States. J. Clim 2006, 19, 763–786.
[14]  Wang, W.; Anderson, B.T.; Phillips, N.; Kaufmann, R.K.; Potter, C.; Field, M. Feedbacks of vegetation on summertime climate variability over the North American grasslands. Part I : Statistical analysis. Earth Interact 2006, 10, 1–27.
[15]  Brunsell, N.A. Characterization of land-surface precipitation feedback regimes with remote sensing. Remote Sens. Environ 2006, 100, 200–211.
[16]  Twine, T.E.; Kucharik, C.J.; Foley, J.A. Effects of land cover change on the energy and water balance of the Mississippi River Basin. J. Hydrol 2004, 5, 640–655.
[17]  Lunetta, R.; Knight, J.; Ediriwickrema, J.; Lyon, J.; Worthy, L. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens. Environ 2006, 105, 142–154.
[18]  Heisler, J.L.; Briggs, J.M.; Knapp, A.K. Long-term patterns of shrub expansion in a C4-dominated grassland: Fire frequency and the dynamics of shrub cover and abundance. Am. J. Bot 2003, 90, 423–428.
[19]  Adegoke, J.; Sr, R.P.; Eastman, J. Impact of irrigation on midsummer surface fluxes and temperature under dry synoptic conditions: A regional atmospheric model study of the US High Plains. Mon. Wea. Rev 2003, 131, 556–564.
[20]  Yang, W.; Yang, L.; Merchant, J.W. An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A. Int. J. Remote Sens 1997, 18, 2161–2180.
[21]  Wang, J.; Price, K.; Rich, P. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. Int. J. Remote Sens 2001, 22, 3827–3844.
[22]  Wang, J.; Rich, P.M.; Price, K.P. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int. J. Remote Sens 2003, 24, 2345–2364.
[23]  Mishra, A.K.; ?zger, M.; Singh, V.P. An entropy-based investigation into the variability of precipitation. J. Hydrol 2009, 370, 139–154.
[24]  Koutsoyiannis, D. Uncertainty, entropy, scaling and hydrological statistics. 1. Marginal distributional properties of hydrological processes and state scaling. Hydrol. Sci. J 2005, 50, 381–404.
[25]  Brunsell, N.A.; Young, C.B. Land surface response to precipitation events using MODIS and NEXRAD data. Int. J. Remote Sens 2008, 29, 1965–1982.
[26]  Brunsell, N. A multiscale information theory approach to assess spatialCtemporal variability of daily precipitation. J. Hydrol 2010, 385, 165–172.
[27]  Brunsell, N.A.; Anderson, M.C. Characterizing the multiCscale spatial structure of remotely sensed evapotranspiration with information theory. Biogeosciences 2011, 8, 2269–2280.
[28]  Stoy, P.C.; Williams, M.; Disney, M.; Prieto-Blanco, A.; Huntley, B.; Baxter, R.; Lewis, P. Upscaling as ecological information transfer: A simple framework with application to Arctic ecosystem carbon exchange. Landsc. Ecol 2009, 24, 971–986.
[29]  Elsner, J.; Tsonis, A. Complexity and predictability of hourly precipitation. J. Atmos. Sci 1993, 50, 400–405.
[30]  Silva, M.E.S.; Carvalho, L.M.V.; da Silva Dias, M.A.F.; Xavier, T.D.M.B.S. Nonlinear processes in geophysics complexity and predictability of daily precipitation in a semi-arid region: An application to Ceara, Brazil. Nonlinear Processes Geophys 2006, 13, 651–659.
[31]  Ji, L.; Peters, A.J. A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains. Int. J. Remote Sens 2004, 25, 297–311.
[32]  Küchler, A.W. A new vegetation map of kansas. Ecology 1974, 55, 586–604.
[33]  Twine, T.E.; Kucharik, C.J. Evaluating a terrestrial ecosystem model with satellite information of greenness. J. Geophys. Res 2008, 113, 1–16.
[34]  Beck, H.E.; McVicar, T.R.; van Dijk, A.I.; Schellekens, J.; de Jeu, R.A.M.; Bruijnzeel, L.A. Global evaluation of four AVHRRCNDVI data sets: Intercomparison and assessment against Landsat imagery. Remote Sens. Environ 2011, 115, 2547–2563.
[35]  Tucker, C.; Pinzon, J.; Brown, M.; Slayback, D.; Pak, E.; Mahoney, R.; Vermote, E.; El Saleous, N. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens 2005, 26, 4485–4498.
[36]  Lokke, D.H.; Kidman, R.O.Y.L. Bibliography of kansas meteorology: Precipitation. Trans. Kansas Acad. Sci 1963, 66, 417–425.
[37]  Williams, C.N., Jr.; Vose, R.S.; Easterling, D.R.; Menne, M.J. United States Historical Climatology Network Daily Temperature, Precipitation, and Snow Data. Technical Report ORNL/CDIAC-118, NDP-070;; Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory: Oak Ridge, TN, USA, 2006.
[38]  Logan, K.; Brunsell, N.; Jones, A.; Feddema, J. Assessing spatiotemporal variability of drought in the U.S. central plains. J. Arid Environ 2010, 74, 247–255.
[39]  USDA-NASS (United State Department of Agricultural-National Agricultural Statistics Service). Available online: http://www.nass.usda.gov/StatisticsbySubject/index.php?sector=CROPS (accessed on 25 May 2013).
[40]  Foley, J.A.; Kucharik, C.J.; Twine, T.E.; Coe, M.T.; Donner, S.D. Land use, land cover, and climate change across the Mississippi basin: Impacts on selected land and water resources. Ecosyst. Land Use Chang 2004, 153, 249–261.
[41]  KARS (Kansas Applied Remote Sensing). Available online: http://kars.ku.edu/research/2005-kansas-land-cover-patterns-level-iv/ (accessed on 20 April 2013).
[42]  GreenReport; Kansas Applied Remote Sensing. Available online: http://kars.ku.edu/geodata/maps/greenreport/ (accessed on 20 April 2013).
[43]  Sifuzzaman, M.; Islam, M.R.; Ali, M.Z. Application of wavelet transform and its advantages compared with fourier transform. J. Phys. Sci 2009, 13, 121–134.
[44]  Brunsell, N.A.; Gillies, R.R. Length scale analysis of surface energy fluxes derived from remote sensing. J. Hydrometeorol 2003, 4, 1212–1219.
[45]  Kumar, P.; Foufoula-georgiou, E. Wavelet analysis for geophysical applications. Rev. Geophys 1997, 35, 385–412.
[46]  Lau, K.M.; Weng, H. Climate signal detection using wavelet transform: How to make a time series sing. Bull. Am. Meteorol. Soc 1995, 76, 2391–2402.
[47]  Shannon, C.; Weaver, W. The Mathematical Theory of Communication; The Board of Trustees of the University of Illinois: Champaign, IL, USA, 1949; p. 132.
[48]  Shannon, C. A mathematical theory of communication, Part 1. Bell Syst. Tech. J 1948, 27, 379–423.
[49]  Shannon, C. A mathematical theory of communication, Part 2. Bell Syst. Tech. J 1948, 27, 623–656.
[50]  Cover, T.M.; Thomas, J.A. Elements of Information Theory; John Wiley & Sons, Inc: New York, NY, USA, 1991.
[51]  Kleeman, R. Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci 2002, 59, 2057–2072.
[52]  Nicholson, S.; Farrar, T. The influence of soil type on the relationship between NDVI, rainfall, and soil moisture in semiarid Botswana: I. NDVI response to rainfall. Remote Sens. Environ 1994, 50, 107–120.
[53]  Unger, P.W. Erosion potential of a terrertic paleustoll after converting conservation reserve program grassland to cropland. Soil Sci. Soc. Am. J 1999, 63, 1795–1801.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133