Waveform LiDAR on Phenology Monitoring and Modeling

I have been doing a lot of research on the concepts and applications of space-borne waveform LiDARs for terrestrial phenology monitoring and modeling. So far the results are very promising. I would want to share my list of references to those wanting to know more about phenology concepts and LiDAR applications on forest phenology. Most of these papers are free online. Find them using Google scholar.

You can leave comments if there are others resources you think may be helpful.

Abdalati WH, Zwally J, Bindschadler R, Csatho B, Farrell SL, et al. 2010. The ICESat-2 Laser Altimetry Mission. Proc. of the IEEE, 98 (5). 10.1109/JPROC.2009.2034765

Ahl DE, Gower SN, Burrows SN, Shabanov NV, Myneni RV, Knyazikhin Y. 2006. Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ. 104: 88– 95

Andersen HE. 2009. Using airborne LiDAR to characterize forest stand condition on the Kenai Peninsula of Alaska. WJAF 24: 95-102

Anderson JE, Plourde LC, Martin ME, Braswell BH, Smith ML, et al. 2008. Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest. Remote Sens. Environ. 112: 1856−70

Andersson K, Evans TP, Richards KR. 2009. National forest carbon inventories: Policy needs and assessment capacity. Climatic Change 93: 69–101

Arp H, Griesbach J, Burns J. 1982. Mapping in tropical forests: a new approach using the laser APR. Photogramm. Eng. Rem. S. 48: 91–100

Asner GP, Jones MO, Martin RE, Knapp DE, Hughes RF. 2008. Remote sensing of native and invasive species in Hawaiian forests. Remote Sens. Environ. 112: 1912–26

Badhwar GD. 1984. Automatic corn – soybean classification using Landsat MSS data: II. Early season crop proportion estimation. Remote Sens. Environ. 14: 31– 37

Baldocchi D, Falge E, Gu LH, Olson R, Hollinger D, et al. 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. B Am Meteorol. Soc. 82: 2415–34

Barilotti A, Turco S, Alberti G. 2006. LAI determination in forestry ecosystems by LiDAR data analysis. Workshop on 3D. Remote Sens. For. 14-15/02/2006, BOKU Vienna.

Black T, Chen W, Barr A, Arain M, Chen Z, et al. 2000. Increased carbon sequestration by a boreal deciduous forest in years with a warm spring. Geophys Res Lett 27:1271–74

Blair JB, Rabine DL, Hofton MA. 1999. The laser vegetation imaging sensor: a medium-altitude, digitization-only, airborne laser altimeter for mapping vegetation and topography. ISPRS J. Photogramm. 54: 115-22

Blair JB, Hofton MA, Luthcke S. 2001. Wide-Swath imaging LiDAR development for airborne and spaceborne applications. International Archives of Photogrammetry and Remote Sensing, Volume XXXIV-3/W4 Annapolis, MD, 22-24

Boudreau J, Nelson RF, Margolis HA, Beaudoin A, Guindon L, Kimes DS. 2008. Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec. Remote Sens. Environ. 112: 3876–90

Buddenbaum H, Seeling S. 2008. Characterization of forest stands using full waveform laser scanner and airborne hyperspectral data. SilviLaser. Sept. 17-19, 2008 – Edinburgh, UK

Cannell MGR, Smith R. 1983. Thermal time, chill days and prediction of budburst in Picea sitchensis. J Appl Ecol 20: 951–63

Carlson TN, Perry EM, Schmugge TJ. 1990. Remote estimates of soil moisture availability and fractional vegetation cover for agricultural fields. Agriculture and Forest Meteorology 52: 45-70

Cayan D, Kammerdiener S, Dettinger M, Caprio J, Peterson D. 2001. Changes in the onset of spring in the western United States. B Am Meteorol Soc 82: 399–415

Chmielewski FM, Rotzer T. 2001. Response of tree phenology to climate change across Europe. Agricultural and Forest Meteorology 108: 101-112

Clark ML, Clark DB, Roberts DA. 2004. Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sens. Environ. 91: 68–89

Collin A, Long B, Archambault P. 2010. Salt-marsh characterization, zonation assessment and mapping through a dual-wavelength LiDAR. Remote Sens. Environ. 114: 520–30

de Beurs K, Henebry GM. 2004. Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan. Remote Sens. Environ. 49: 497– 509

de Beurs K, Henebry GM. 2005. A statistical framework for the analysis of long image time series. Int. J. Remote Sens. 26: 1551–1573

de Beurs K, Henebry GM. 2010. Spatio-temporal statistical methods for modeling land surface phenology. Phenological Research: Methods for Environmental and Climate Change Analysis. (Eds. Hudson I.L. & Keatley M.R.). Springer Books, New York.

DeFries R. 2008. Terrestrial vegetation in the coupled human‐earth system: Contributions of remote sensing. Annual Review of Environment and Resources 33: 369–90

Delbart N, Toan TL, 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: 52–62

Drake JB, Dubayah RO, Knox RG, Clark DB, Blair JB. 2002a. Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sens. Environ. 81: 378-92

Drake JB, Dubayah RO, Clark DB, Knox RG, Blair JB et al. 2002b. Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sens. Environ. 79: 305–19

Drake JB, Knox RG, Dubayah RO, Clark DB, Condit R, Blair JB, et al. 2003. Above-ground biomass estimation in closed canopy neotropical forests using lidar remote sensing: Factors affecting the generality of relationships. Global Ecol. Biogeogr. 12: 147–59

Dubayah RO, Blair JB, Bufton JL, Clark DB, Ja´Ja´ J, et al. 1997. The Vegetation Canopy Lidar mission. Proc. Conf. Land Satellite Information in the Next Decade II. American Society for Photogrammetry and Remote Sensing, pp. 100–12

Dubayah RO, Blair JB, Bufton JL, Clark DB, Ja´Ja´ J, et al. 1997. The Vegetation Canopy Lidar mission. Proc. Conf. Land Satellite Information in the Next Decade II. American Society for Photogrammetry and Remote Sensing, pp. 100–12

Dubayah RO, Drake JB. 2000: Lidar remote sensing for forestry. J. Forestry 98: 44–46

Dubayah RO, Knox RG, Hofton MA, Blair JB, Drake JB. 2000. Land surface characterization using lidar remote sensing. in M. Hill and R. Aspinall, eds. Spatial Information for Land Use Management. International Publishers Direct, Singapore

Duchemin B, Goubier J, Courrier G. 1998. Monitoring phenological key stages and cycle duration of temperate deciduous forest ecosystems with NOAA/AVHRR data. Remote Sens. Environ. 67: 68–82

Duchemin B, Guyon D, Lagouarde JP. 1999. Potential and limits of NOAA–AVHRR temporal composite data for phenology and water stress monitoring of temperate forest ecosystems. Int. J. Remote Sens. 20: 895–917

Farid A, Goodrich DC, Sorooshian S. 2006. Using airborne lidar to discern age classes of Cottonwood trees in a riparian area. WJAF 21(3): 149-58

Fisher JI, Mustard JF, Vadeboncoeur MA. 2006. Green leaf phenology at Landsat resolution: Scaling from the field to the satellite. Remote Sens. Environ. 100: 265–279

Fitzjarrald D, Acevedo O, Moore K. 2001. Climatic consequences of leaf presence in the eastern United States. J Climate 14: 598–614

Freeman A, Rosen P, Jordan R, Johnson WTK, Hensley S, et al. 2009. DESDynI – A NASA Mission for ecosystems, solid earth, and cryosphere science. Proceedings of the Pol-InSAR Workshop, Frascati, January 26-30, 2009.

Gaulton R, Danson FM, Pearson G, Lewis PE, Disney M. 2010. The Salford Advanced Laser Canopy Analyser (SALCA): A multispectral full waveform LiDAR for improved vegetation characterisation. Proceeding of the remote sensing and photogrammetry society conference, Remote Sensing and the Carbon Cycle, Burlington House, London, 5 May 2010

Gibbs HK, Brown S, Foley JA, Niles JO. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD and reality. Environ. Res. Lett. 2 045023. doi: 10.1088/1748-9326/2/4/045023

Goetz SJ, Bunn AG, Fiske GJ, Houghton RA. 2005. Satellite‐observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proceedings of the National Academy of Sciences of the United States of America 102(38): 13521–13525.

Gordo O, Sanz JJ. 2010. Impact of climate change on plant phenology in Mediterranean ecosystems. Global Change Biology 16: 1082–106

Goward SN, Markham B, Dye DG, Dulaney W, Yang AJ. 1991. Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer. Remote Sens. Environ. 35: 257–77

Gutman G, Ignatov A. 1998. The derivation of green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens. 19: 1533–43

Hänninen H. 1990. Modeling bud dormancy release in trees from cool and temperate regions. Acta For. Fenn. 213: 1-47

Häninnen H. 1994. Effects of climatic change on trees from cool and temperate regions: an ecophysiological approach to modeling of bud burst phenology. Can J Bot 73: 183–99

Harding DJ, Lefsky MA, Parker GG, Blair JB. 2001. Laser altimeter canopy height profiles: methods and validation for closed canopy, broadleaf forests. Remote Sens. Environ. 76: 283–97

Harding DJ, Carabajal CC. 2005. ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophys. Res. Lett. 32: L21S10, doi:10.1029/2005GL023471

Helmer EH, Lefsky MA, Roberts DA. 2009. Biomass accumulation rates of Amazonian secondary forest and biomass of old-growth forests from Landsat time series and the Geoscience Laser Altimeter System. J. Appl. Remote Sens. 3: DOI: 10.1117/1.3082116.

Hopkinson C, Chasmer L, Hall RJ. 2008. The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets. Remote Sens. Environ. 112: 1168–80

Hopkinson C, Chasmer L. 2007. Modelling canopy gap fraction from LiDAR intensity. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, Espoo, September 12-14, 2007, Finland

Hopkinson C, Chasmer L. 2009. Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sens. Environ. 113: 275−88

Houghton RA. 2005. Aboveground forest biomass and the global carbon balance. Global Change Biology 11: 945–58

Hurtt GC, Dubayah RO, Drake JB, Moorcroft PR, Pacala SW, et al. 2004. Beyond potential vegetation: Combining lidar data and a height structured model for carbon studies. Ecol. Appl. 14: 873-883

Inouye DW. 2008. Effects of climate change on phenology, frost damage, and floral abundance of montane wildflowers. Ecology 89:353–62

Intergovernmental Panel on Climate Change (IPCC). 2007. Climate change 2007: impacts, adaptation, and vulnerability, Chapter 1. Assessment of Observed Changes and Responses in Natural and Managed Systems. IPCC Secretariat. Geneva, Switzerland; http://www.ipcc.ch/pdf/assessment-report/ar4/wg2/ar4-wg2-chapter1.pdf.

Jackson RB, Lechowicz MJ, Li X, Mooney HA. 2001. Phenology, growth, and allocation in global terrestrial productivity. In: Roy J, Saugier B, Mooney HA (eds) Terrestrial global productivity: past, present, and future. Academic, San Diego, 61–82

Jarvis PG, Leverenz JW. 1983. Productivity of temperate, deciduous and evergreen forests. In: Lange OL, Nobel PS, Osmond CB, Zeigler H, eds. Physiological plant ecology IV. Ecosystem processes: mineral cycling productivity and man’s influence. Encyclopedia of Plant Physiology. Vol. 12D. Berlin: Springer Verlag, 233-80

Kaduk J, Heimann M. 1996. A prognostic phenology scheme for global terrestrial carbon cycle models. Climate Research 6: 1–19

Kang S, Running SW, Lim JH, Zhao M, Park CR, Loehman R. 2003. A regional phenology model for detecting onset of greenness in temperate mixed forests, Korea: An application of MODIS leaf area index. Remote Sens. Environ. 86: 232– 42

Kim Y, Wang GL. 2005. Modeling seasonal vegetation variation and its validation against Moderate Resolution Imaging spectroradiometer (MODIS) observations over North America, J. Geophys. Res. 110, D04106, doi:10.1029/2004JD005436

Kim S, McGaughey RJ, Andersen HE, Schreuder G. 2009. Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data. Remote Sens. Environ. 113: 1575–1586

Koetz B, Morsdorf F, Sun G, Ranson KJ, Itten K, Allgower B. 2006. Inversion of a lidar waveform model for forest biophysical parameter estimation. IEEE Geosci. Remote S. 3: 49–53

Koetz B, Sun GQ, Morsdorf F, Ranson KJ, Kneubuhler M, et al. 2007. Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization. Remote Sens. Environ. 106: 449–59

Kogan F. 1995. Droughts of the late 1980s in the United States as derived from NOAA polar-orbiting satellite data. Bull. Am. Meteorol. Soc. 76: 665– 68

Krabill WB, Collins JG, Link LE, Swift RN, Butler ML. 1984. Airborne laser topographic mapping results. Photogramm. Eng. Rem. S. 50: pp. 685-94

Kramer K. 1994. Selecting a model to predict the onset of growth of Fagus sylvatica. J. Appl. Ecol. 31: 172—81

Kramer K, Leinonen I, Loustau D. 2000. The importance of phenology for the evaluation of impact of climate change on growth of boreal, temperate and Mediterranean forests ecosystems: An overview. Int. J. Biometeorol. 44: 67– 75

Lefsky MA, Cohen WB, Acker SA, Parker GG, Spies TA, Harding D. 1999a. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sens. Environ. 70: 339–61

Lefsky MA, Harding D, Cohen WB, Parker GG. 1999b. Surface lidar remote sensing of the basal area and biomass in deciduous forests of eastern Maryland, USA. Remote Sens. Environ. 67: 83-98

Lefsky MA, Cohen WB, Parker GG, Harding DJ. 2002. LIDAR remote sensing for ecosystem studies. Bioscience. 52: 19–30

Lefsky MA, Cohen WB, Harding DJ, Parker GG, Acker SA, Gower ST. 2002b. Lidar remote sensing of above-ground biomass in three biomes. Global Ecol. Biogeogr. 11: 393–99

Lefsky MA, Hudak AT, Cohen WB, Acker SA. 2005. Geographic variability in lidar predictions of forest stand structure in the Pacific Northwest. Remote Sens. Environ. 95: 532–48

Liang L, Schwartz MD. 2009: Landscape phenology: an integrative approach to seasonal vegetation dynamics. Landscape Ecology 24: 465–72

Lieth H. 1974. Phenology and seasonality modelling. Berlin, Germany: Springer.

Lim KS, Treitz PM, Wulder M, St-Onge B, Flood M. 2003. Lidar remote sensing of forest structure. Prog. Phys. Geog. 27: 88–106

Lim KS, Treitz PM. 2004. Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scand. J. For. Res. 19: 558–70

Lloyd, D. (1990), A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. Int. J. Remote Sens. 11: 2269– 79

Lucas RM, Lee AC, Bunting PJ. 2008. Retrieving forest biomass through integration of CASI and LiDAR data. Int. J. Remote Sens. 29: 1553–77

Magnussen S, Eggermont P, LaRiccia VN. 1999. Recovering tree heights from airborne laser scanner data. Forest Sci. 45: 407-22

Martens SN, Ustin SL, Rousseau RA. 1993. Estimation of tree canopy leaf area index by gap fraction analysis. For. Ecol. Manage. 61: 91-108

Martinuzzi S, Vierling LA, Gould WA, Falkowski MJ, Evans JS, et al. 2009. Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability. Remote Sens. Environ. 113: 2533–46

Menzel A, Fabian P. 1999. Growing season extended in Europe. Nature 397:659

Menzel A. 2000. Trends in phenological phases in Europe between 1951 and 1996. Int. J. Biometeorol. 44: 76–81

Menzel A, Estrella N. 2001. Plant phenological changes. In “Fingerprints” of Climate Change — Adapted Behaviour and Shifting Species Ranges, Walther G-R, Burga CA, Edwards PJ (eds). Kluwer Academic/Plenum Publishers: New York; 123–37

Miller-Rushing AJ, Primack RB. 2008. Global warming and flowering times in Thoreau’s Concord: a community perspective. Ecology 89: 332–41

Miura N, Jones SD. 2010. Characterizing forest ecological structure using pulse types and heights of airborne laser scanning. Remote Sens. Environ. 114: 1069-76

Morin X, Jacques R, Sonie L, Chuine I. 2010. Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytologist 186: 900-10

Morton DC, DeFries RS, Shimabukuro YE, Anderson LO, Espirito‐Santo FDB et al. 2005. Rapid assessment of annual deforestation in the Brazilian Amazon using MODIS data. Earth Interactions 9(8): 1–22

Moulin S, Kergoat L, Viovy N, Dedieu G. 1997. Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J. Clim. 10: 1154– 70

Murray MB, Cannel MGR, Smith RI. 1989. Date of bud burst of fifteen tree species in Britain following climatic warming. J Appl Ecol 26: 693–00

Myneni RB, Keeling CD, Tucker CJ, Asrar G, Nemani RR. 1997. Increased plant growth in the northern high latitudes from 1981– 1991. Nature 386: 698–02

Naesset E. 1997. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J. Photogramm. Remote Sens. 52: 49 –56

NASA (National Aeronautics and Space Administration). 2009. IceSat Homepage. http://icesat.gsfc.nasa.gov/index.php (accessed July 22, 2010)

National Research Council. 2007. Earth Science and Applications from Space: National imperatives for the next decade and beyond, executive summary. National Academies Press. 43 pgs. [downloadable pdf at http://www.nap.edu/catalog/11820.html, la= July 19, 2010]

Nelson R, Ranson KJ, Sun G, Kimes DS, Kharuk V, Montesano P. 2009. Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sens. Environ. 113(3): 691–01

Nilson T. 1971. Theoretical analysis of frequency of gaps in plant stands. Agricultural Meteorology 8: 25−38

Oberto B, Loverro A, Hu S, Blair B. 2008. Mission and Spacecraft Configuration Studies (Priority Tasks 1, 2, & 4). DESDynI Science Study Group Meeting. June 2008, Greenbelt, MD.

Parker GG, Harding DJ, Berger ML. 2004. A portable LiDAR system for rapid determination of forest canopy structure. J. Appl. Ecol. 41(4): 755–67

Parmesan C. 2006. Ecological and evolutionary responses to recent climate change. Ann. Rev. Ecol. Evol. System. 37: 637–69

Peñuelas J, Filella I. 2001. Phenology: responses to a warming world. Science 294: 793–95

Peñuelas J. Rutishauser T, Filella I. 2009. Phenology feedbacks on climate change, Science, 324(5929), 887–888, doi:10.1126/science. 1173004

Powell SL, Cohen WB, Healey SP, Kennedy RP, Moisen GG et al. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches, Remote Sens. Environ. doi:10.1016/j.rse.2009.12.018.

Raman CV. 1928. A New Radiation. Indian Jour. Phys. 2: 387

Rathcke B, Lacey EP. 1985. Phenological patterns of terrestrial plants. Ann. Rev. Ecol Syst. 16: 179-214

Reed BC, White M, Brown JF. 2003. Remote sensing phenology. In: (M.D. Schwartz, ed.) Phenology: An Integrative Environmental Science, pp. 365–381. Kluwer: Dordrecht, the Netherlands.

Reed BC, Brown JF, VanderZee D, Loveland TR, Merchant JW, Ohlen DO. 1994. Measuring phenological variability from satellite imagery. Journal of Vegetation Science 5: 703– 14

Reutebuch SE, Andersen HE, McGaughey RJ. 2005. Light detection and ranging (LIDAR): An emerging tool for multiple resource inventory. J. Forest. 103(6): 286-92

Rignot E, Echelmeyer E, Krabill K. 2001. Penetration depth of interferometric synthetic-aperture radar signals in snow and ice. Geophys. Res. Lett. 28: 3501–04

Robin J, Dubayah R, Sparrow E, Levine E. 2007: Monitoring start of season in Alaska with GLOBE, AVHRR, and MODIS data. J. Geophys. Res. 113: G01017, doi:10.1029/2007JG000407

Rosenqvist A, Milne A, Lucas R, Imhoff M, Dobson C. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environ. Sci Pol 6(5): 441–55

Sakamoto T, Yokozawa M, Toritani H, Shibayama M, Ishitsuka N, Ohno H. 2005. A crop phenology detection method using time-series MODIS data. Remote Sens. Environ. 96, 366–74

Schaber J, Badeck FW. 2003. Physiology-based phenology models for forest tree species in Germany. Int. J. Biometeorol 47: 193– 201

Schmid HP, Su H-B, Vogel CS, Curtis PS. 2003. Ecosystem-atmosphere exchange of carbon dioxide over a mixed hardwood forest in northern lower Michigan. J Geophys Res DOI 10.1029/2002JD003011

Schwartz MD, Ahas R, Aasa A. 2006. Onset of spring starting earlier across the Northern Hemisphere. Glob Change Biol 12: 343–51

Schwartz MD, Hanes JM. 2009. Continental-scale phenology: warming and chilling. Int. J. Climatol. Wiley InterScience. DOI: 10.1002/joc.2014

Sun GQ, Ranson KJ. 2000. Modeling lidar returns from forest canopies. IEEE Trans. Geosci. Remote Sens. 38(6): 2617–26

Suzuki R, Nomaki T, Yasunari T. 2003. West-east contrast of phenology and climate in northern Asia revealed using a remotely sensed vegetation index, Int. J. Biometeorol. 47: 126–38

Tarpley JP, Schneider SR, Money RL. 1984. Global vegetation indices from NOAA-7 meteorological satellite. J Climate App Meteorol 23: 491–94

Thoman R, Fathauer T. 1998. An investigation into estimating green-up dates around Fairbanks Alaska using thermal indices, Natl. Weather Serv.

Urban DL. 1990. A versatile model to simulate forest pattern: A user guide to ZELIG, version 1.0. Charlottesville, VA: Environ. Sci. Dept. Univ. Virginia

Ustin SL, Roberts DA, Gamon JA, Asner GP, Green RO. 2004. Using imaging spectroscopy to study ecosystem processes and properties. Bioscience 54(6): 523–534

Vierling KT, Vierling LA, Gould W, Martinuzzi S, Clawges R. 2008. Lidar: Shedding new light on habitat characterization and modeling. Front. Ecol. Environ. 6(2): 90–98
White MA, Running SW, Thornton PE. 1997. A continental phenology model for monitoring vegetation responses to inter-annual climate variability. Global Biogeochem Cycles 11: 217–34

White MA, Running SW, Thornton PE. 1999. The impact of growing season length variability on carbon assimilation and evapo-transpiration over 88 years in the eastern US deciduous forest. Int J Biometeorol 42: 139–45

White MA, Nemani RR, Thornton PE, Running SW. 2002. Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems 5: 260–73

White MA, Nemani RR. 2003. Canopy duration has little influence on annual carbon storage in the deciduous broad leaf forest. Global Change Biology 9: 967–72

White MA, Brunsell NA, Schwartz MD. 2003. Vegetation phenology in global change studies. In: Schwartz MD (ed) Phenology: an integrative environmental science. Kluwer, Dordrect, pp 453–466

White MA, de Beurs KM, Didan K, Inouye DW, Richardson AD, Jensen OP et al. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Global Change Biology, doi: 0.1111/j.1365-2486.2009.01910.x.

Zhang X, Friedl MA, Schaaf CB et al. 2003. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84: 471–75
Zhang X, Friedl MA, Schaaf CB, Strahler AH. 2004. Climate controls on vegetation phenological patterns in northern mid-and high latitudes inferred from MODIS data. Global Change Biology, 10, 1–13. doi:10.1111/j.1529-8817.2003.00784.x

Zhao KG, Popescu S. 2009. LiDAR-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in temperate forest of the southern USA. Remote Sens. Environ. 113: 1628-1645.

Zhao KG, Popescu S, Nelson R. 2009. Lidar remote sensing of forest biomass: A scaleinvariant estimation approach using airborne lasers. Remote Sens. Environ. 113(1): 182–96

Zhou L, Tucker CJ, Kaufmann RK, Slayback D, Shabanov NV, Myneni RB. 2001. Variation in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res. 106(D17), 20,069–20,083

Zimble DA, Evans DL, Carson GC, Parker RC, Grado SC, Gerard PD. 2003. Characterizing vertical forest structure using small-footprint airborne lidar. Remote Sens. Environ. 87: 171–82