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5 - Remote Sensing for Mapping and Modeling of Land-Based Carbon Flux and Storage

Published online by Cambridge University Press:  05 February 2013

Daniel G. Brown
Affiliation:
University of Michigan, Ann Arbor
Derek T. Robinson
Affiliation:
University of Waterloo, Ontario
Nancy H. F. French
Affiliation:
Michigan Technological University
Bradley C. Reed
Affiliation:
United States Geological Survey, California
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Summary

Introduction

An essential aspect of carbon (C) accounting is the development of methods and technologies for measurement and monitoring of C pools and fluxes. Forest and agricultural systems are key to the C cycle, as they hold and rapidly exchange large amounts of C, and human-influenced dynamics of C in these systems are very large. Wetlands, streams, and rivers are important reservoirs and exchange points for C, with C in land and hydrologic systems vulnerable to land-use impacts and other natural disturbance forces. In the context of climate change, the sizes of C pools and magnitudes of C fluxes (see Chapter 2) need to be both well understood for modeling purposes and accurately monitored to quantify and attribute changes driven by land-change processes and confounded by climate-change forces.

Direct-measurement methods for C accounting, such as a ground-based inventories, can be inappropriate for covering large landscapes to document extensive C pools or for repeating measurements needed to adequately account for C dynamics. However, if properly deployed, remote sensing systems can be used to provide the spatially synoptic and temporally frequent coverage needed to document land conditions and changes over time (Cohen and Goward 2004; Houghton and Goetz 2008). Remote sensing tools and techniques have developed since the first airborne sensors (photographic cameras) were deployed in the early 1900s. They have progressed from simple passive recording devices to advanced passive and active sensing systems operating from airborne and spaceborne platforms. Remote sensing science includes the data collection technologies and data analysis techniques developed to use remotely sensed data within the framework of spatial data analyses.

Type
Chapter
Information
Land Use and the Carbon Cycle
Advances in Integrated Science, Management, and Policy
, pp. 95 - 143
Publisher: Cambridge University Press
Print publication year: 2013

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References

Aase, J.K., and Tanaka, D.L. 1991. Reflectance from four wheat residue cover densities as influenced by three soil backgrounds. Agronomy Journal, 83:753–757.CrossRefGoogle Scholar
Aitkenhead, J.A., and McDowell, W. 2000. Soil C:N ratio as a predictor of annual riverine DOC flux at local and global scales. Global Biogeochemical Cycles, 14:127–138.CrossRefGoogle Scholar
Amiro, B.D., Barr, A.G., Black, T.A., Bracho, R., Brown, M., Chen, J.,…Xiao, J. 2010. Ecosystem carbon dioxide fluxes after disturbance in forests of North America. Journal of Geophysical Research, 115:G00K02, .CrossRefGoogle Scholar
Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E. 1976. A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper, no. 964, Washington, DC: U.S. Geological Survey.Google Scholar
Anderson, M.C., Norman, J.M., Kustas, W.P., Houborg, R., Starks, P.J., and Agam, N. 2008. A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112:4227–4241, .CrossRefGoogle Scholar
Asrar, G., Kanemasu, E.T., Jackson, R.D., and Pinter, P.J. 1985. Estimation of total above-ground phytomass production using remotely sensed data. Remote Sensing of Environment, 17:211–220.CrossRefGoogle Scholar
Augustein, M., and Warrender, C. 1998. Wetland classification using optical and radar data and neural network classification. International Journal of Remote Sensing, 19:1545–1560.CrossRefGoogle Scholar
Austin, J.M., Mackey, B.G., and Van Niel, K.P. 2003. Estimating forest biomass using satellite radar: An exploratory study in a temperate Australian Eucalyptus forest. Forest Ecology and Management, 176:575–583.CrossRefGoogle Scholar
Baccini, A., Laporte, N.T., Goetz, S.J., Sun, M., and Dong, H. 2008. A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environmental Research Letters, 3(4):045011.CrossRefGoogle Scholar
Balch, W.M., Drapeau, D.T., Fritz, J.J., Bowler, B.C., and Nolan, J. 2001. Optical backscattering in the Arabian Sea: Continuous underway measurements of particulate inorganic and organic carbon. Deep Sea Research, 48:2423–2452.CrossRefGoogle Scholar
Barger, N.N., Archer, S.R., Campbell, J.L., Huang, C., Morton, J.A., and Knapp, A.K. 2011. Woody plant proliferation in North America drylands: A synthesis of impacts on ecosystem carbon balance. Journal of Geophysical Research, 116:G00K07, .CrossRefGoogle Scholar
Beaudoin, A., Le Toan, T., Goze, S., Nezry, E., Lopes, A., Mougin, E.,…Shin, R.T. 1994. Retrieval of forest biomass from SAR data. Journal of Remote Sensing, 15:2777–2796.CrossRefGoogle Scholar
Becker, B.L., Lusch, D.P., and Qi, J. 2007. A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery. Remote Sensing of Environment, 108:111–120.CrossRefGoogle Scholar
Behrenfeld, M.J., Boss, E., Siegel, D.A., and Shea, D.M. 2005. Carbon-based ocean productivity and phytoplankton physiology from space. Global Biogeochemical Cycles, 19:GB1006, .CrossRefGoogle Scholar
Behrenfeld, M.J., Randerson, J.T., McClain, C.R., Feldman, G.C., Los, S.O., Tucker, C.J.,…Pollack, N.H. 2001. Biospheric primary production during an ENSO transition. Science, 291:2594–2597.CrossRefGoogle ScholarPubMed
Ben-Dor, E., Irons, J.R., and Epema, J.F. 1999. Soil reflectance. In Manual of remote sensing for the earth sciences, 3d ed., ed. Rencz, A.N.. New York: Wiley, pp. 111–188.Google Scholar
Bergen, K.M., Goetz, S., Dubayah, R., Henebry, G., Hunsaker, C.T., Imhoff, M.,…Radeloff, V.C. 2009. Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research, 114:G00E06, .CrossRefGoogle Scholar
Binding, C.E., Bowers, D.G., and Mitchelson-Jacob, E.G. 2003. An algorithm of suspended sediment concentrations in the Irish Sea from SeaWiFS ocean colour satellite imagery. International Journal of Remote Sensing, 24:3791–3806.CrossRefGoogle Scholar
Blackard, J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus, M.L., Jacobs, D.M.,…Tymcio, R.P. 2008. Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information. Remote Sensing of Environment, 112:1658–1677.CrossRefGoogle Scholar
Blaschke, T., Johansen, K., and Tiede, D. 2011. Object-based image analysis for vegetation mapping and monitoring. In Advances in environmental remote sensing: Sensors, algoritms, and applications, ed. Weng, Q.. Boca Raton, FL: CRC Press, pp. 241–271.CrossRefGoogle Scholar
Boss, E., Twardowski, M.S., and Herring, S. 2001. The shape of the particulate beam attenuation spectrum and its relation to the size distribution of oceanic particles. Applied Optics, 40:4885–4893.CrossRefGoogle Scholar
Boudreau, J., Nelson, R.F., Margolis, H.A., Beaudoin, A., Guindon, L., and Kimes, D.S. 2008. Regional aboveground forest biomass using airborne and spaceborne LiDAR in Quebec. Remote Sensing of Environment, 112:3876–3890.CrossRefGoogle Scholar
Bourgeau-Chavez, L.L., Harrell, P.A., Kasischke, E.S., and French, N.H.F. 1997. The detection and mapping of Alaskan wildfires using a spaceborne imaging radar system. International Journal of Remote Sensing, 18:355–373.CrossRefGoogle Scholar
Bourgeau-Chavez, L.L., Kasischke, E.S., Brunzell, S.M., Mudd, J.P., Smith, K.B., and Frick, A.L. 2001. Analysis of spaceborne SAR data for wetland mapping in Virginia riparian ecosystems. International Journal of Remote Sensing, 22:3665–3687.CrossRefGoogle Scholar
Bourgeau-Chavez, L.L., Kasischke, E.S., Riordan, K., Brunzell, S.M., Hyer, E., Nolan, M.,…Ames, S. 2007. Remote monitoring of spatial and temporal surface soil moisture in fire disturbed boreal forest ecosystems with ERS SAR imagery. International Journal of Remote Sensing, 28:2133–2162.CrossRefGoogle Scholar
Bourgeau-Chavez, L.L., Lopez, R.D., Trebitz, A., Hollenhorst, T., Host, G.E., Huberty, B.,…Hummer, J. 2008. Landscape-based indicators. In Great Lakes coastal wetlands monitoring plan. Great Lakes Coastal Wetlands Consortium, Project of the Great Lakes Commission, funded by the U.S. EPA GLNPO, pp. 143–171. .
Bourgeau-Chavez, L.L., and Powell, R. 2009. Mapping the invasive phragmites with ALOS PALSAR radar imagery over the Saint Clair River Delta in the Great Lakes. Society of Wetland Scientists 2009 Conference, Madison, WI, June 21–26, 2009.
Bourgeau-Chavez, L.L., Riordan, K., Nowels, M., and Miller, N. 2004. Final report to the Great Lakes Commission: Remotely monitoring Great Lakes coastal wetlands using a hybrid radar and multi-spectral sensor approach. Project no. WETLANDS2-WPA-06. - landscapeReport.pdf.
Bourgeau-Chavez, L.L., Riordan, K., Powell, R.B., Miller, N., and Nowels, M. 2009. Improving wetland characterization with multi-sensor, multi-temporal SAR and optical/infrared data fusion. In Advances in Geoscience and Remote Sensing, ed. Jedlovec, G.. India: InTech, pp. 679–708.Google Scholar
Brenner, J., Paustian, K., Bluhm, G., Cipra, J., Easter, M., Foulk, R.,…Williams, S. 2002. Quantifying the change in greenhouse gas emissions due to natural resource conservation practice application in Nebraska. Colorado State University, Natural Resources Ecology Laboratory, and USDA Natural Resources Conservation Service, Fort Collins, Colorado.Google Scholar
Bricaud, A., Morel, A., and Prieur, L. 1981. Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains. Limnology and Oceanography, 26:43–53.CrossRefGoogle Scholar
Bridgham, S.D., Megonigal, J.P., Keller, J.K., Bliss, N.B., and Trettin, C. 2006. The carbon balance of North American wetlands. Wetlands, 26:889–916.CrossRefGoogle Scholar
Brown, D.G., Pijanowski, B.C., and Duh, J.-D. 2000. Modeling the relationships between land-use and land-cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59:247–263.CrossRefGoogle Scholar
Brown, J.F., Bourgeau-Chavez, L.L., Riordan, K., Garwood, G., Slawski, J., Alden, S.,…Kwart, M. 2005. Assessing fuel moisture with satellite imaging radar for improved fire danger prediction in boreal Alaska. Eos, Transactions, American Geophysical Union, 86(52), Fall Meeting Supplement, G13A-02.Google Scholar
Chapin, F.S., Woodwell, G.M., Randerson, J.T., Rastetter, E.B., Lovett, G.M., Baldocchi, D.D.,…Valentini, R. 2006. Reconciling carbon-cycle concepts, terminology, and methods. Ecosystems, 9:1041–1050.CrossRefGoogle Scholar
Chen, F., Kissel, D.E., West, L.T., Rickman, D., Luvall, J.C., and Adkins, W. 2005. Mapping surface soil organic carbon for crop fields with remote sensing. Journal of Soil and Water Conservation, 60:51–57.Google Scholar
Chubey, M.S., Franklin, S.E., and Wulder, M.A. 2006. Object-based analysis of IKONOS imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing, 72:383–394.CrossRefGoogle Scholar
Cihlar, J. 2000. Land cover mapping of large areas from satellites: Status and research priorities. International Journal of Remote Sensing, 21:1093–1114.CrossRefGoogle Scholar
Cihlar, J., and Jansen, L.J.M. 2001. From land cover to land use: A methodology for efficient land use mapping over large areas. Professional Geographer, 53:275–289.CrossRefGoogle Scholar
Cohen, W.B., and Goward, S.N. 2004. Landsat's role in ecological applications of remote sensing. BioScience, 54:535–545.CrossRefGoogle Scholar
Creed, I., Sanford, S., Beall, F., Molot, L., and Dillon, P. 2003. Cryptic wetlands: Integrating hidden wetlands in regression models of the export of dissolved organic carbon from forested landscapes. Hydrological Processes, 17:3629–3648, .CrossRefGoogle Scholar
Cronan, C., Piampiano, J., and Patterson, H. 1999. Influence of land use and hydrology on exports of carbon and nitrogen in a Maine River Basin. Journal of Environmental Quality, 28:953–961.CrossRefGoogle Scholar
Currie, W.S., Yanai, R.D., Piatek, K.B., Prescott, C.E., and Goodale, C.L. 2003. Processes affecting carbon storage in the forest floor and in downed woody debris. In The potential of U.S. forest soils to sequester carbon and mitigate the greenhouse effect, ed. Kimble, J.M., Heath, L.S., Birdsey, R.A., and Lal. Boca, R.Raton, FL: CRC Press, pp. 135–157.Google Scholar
Daughtry, C.S.T. 2001. Discriminating crop residues from soil by shortwave infrared reflectance. Agronomy Journal, 93:125–131.CrossRefGoogle Scholar
Daughtry, C.S.T., Hunt, E.R, and McMurtrey, J.E. III. 2004. Assessing crop residue cover using shortwave infrared reflectance. Remote Sensing of Environment, 90:126–134.CrossRefGoogle Scholar
Daughtry, C.S.T., Hunt, E.R.J., Doraiswamy, P.C., and McMurtrey, J.E. III. 2005. Remote sensing the spatial distribution of crop residues. Agronomy Journal, 97:864–871.CrossRefGoogle Scholar
Dawson, J., Soulsby, C., Tetzlaff, D., Hrachowitz, M., Dunn, S., and Malcolm, I. 2008. Influence of hydrology and seasonality on DOC exports from three contrasting upland catchments. Biogeochemistry, 90:93–113, .CrossRefGoogle Scholar
DeFries, R.S., and Chan, J.C. 2000. Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data. Remote Sensing of Environment, 74(3):503–515.CrossRefGoogle Scholar
Del Castillo, C., and Miller, R. 2008. On the use of ocean color remote sensing to measure the transport of dissolved organic carbon by the Mississippi River plume. Remote Sensing of Environment, 112:838–844.CrossRefGoogle Scholar
DiGregorio, J. 2005. Land Cover Classification System (LCCS): Classification concepts and user manual – version 2. Environment and Natural Resources Service Series, no. 8, Rome: FAO.Google Scholar
Dlugokencky, E.J., Nisbet, E.G., Fisher, R., and Lowrey, D. 2011. Global atmospheric methane: Budget, changes and dangers. Philosophical Transactions of the Royal Society. Series A, Mathematical, Physical, and Engineering Sciences, 369:2058–2072.CrossRefGoogle ScholarPubMed
Dobson, M.C., and Ulaby, F.T. 1998. Mapping soil moisture distribution with imaging radar. In Principles and applications of imaging radar, manual of remote sensing, 3d ed., vol. 2, ed. Henderson, F.M.. New York: John Wiley and Sons, pp. 407–433.Google Scholar
Dobson, M.C., Ulaby, F.T., Le Toan, T., Beaudoin, A., Kasischke, E.S., and Christensen, N.L. Jr. 1992. Dependence of radar backscatter on coniferous forest biomass. IEEE Transactions on Geoscience and Remote Sensing, 30:412–415.CrossRefGoogle Scholar
Dobson, M.C., Ulaby, F.T., Pierce, L.E., Shank, T.L., Bergen, K.M., Kellndorfer, J.,…Siqueira, P. 1995. Estimation of forest biomass characteristics in northern Michigan with SIR-C/X-SAR data. IEEE Transactions on Geoscience and Remote Sensing, 33:877–894.CrossRefGoogle Scholar
Dolan, K., Masek, J.G., Huang, C., and Sun, G. 2009. Regional forest growth rates measured by combining ICESat GLAS and Landsat data. Journal of Geophysical Research, 114:G00E05, .CrossRefGoogle Scholar
Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A.,…Prince, S. 2002. Estimation of tropical forest structural characteristics using large-footprint LiDAR. Remote Sensing of Environment, 79:305–319.CrossRefGoogle Scholar
Dubayah, R.O., Sheldon, S.L., Clark, D.B., Hofton, M.A., Blair, J.B., and Chazdon, R.L. 2010. Estimation of tropical forest height and biomass dynamics using lidar remote sensing. Journal of Geophysical Research, 115:GE00E09, .CrossRefGoogle Scholar
Eimers, M., Buttle, J., and Watmough, S. 2008. Influence of seasonal changes in runoff and extreme events on dissolved organic carbon trends in wetland- and upland-draining streams. Canadian Journal of Fisheries and Aquatic Sciences, 65:796–808, .CrossRefGoogle Scholar
Estapa, M., Mayer, L., Boss, E., and Roesler, C. 2012. Role of iron and organic carbon in mass-specific light absorption by particulate matter from Louisiana coastal waters. Limnology and Oceanography, 57:97–112, .CrossRefGoogle Scholar
Evans, C., Monteith, D., and Cooper, D. 2005. Long-term increases in surface water dissolved organic carbon: Observations, possible causes and environmental impacts. Environmental Pollution, 137:55–71, .CrossRefGoogle ScholarPubMed
Evans, J.S., and Hudak, A.T. 2007. A multiscale curvature algorithm for classifying discrete return lidar in forested environments. IEEE Transactions on Geoscience and Remote Sensing, 45:1029–1038.CrossRefGoogle Scholar
Falkowski, M.J., Evans, J.S., Martinuzzi, S., Gessler, P.E., and Hudak, A.T. 2009a. Characterizing forest succession with Lidar data: An evaluation for the inland Northwest USA. Remote Sensing of Environment, 113:946−956.CrossRefGoogle Scholar
Falkowski, M.J., Smith, A.M.S., Gessler, P.G., Hudak, A.T., Vierling, L.A., and Evans, J.S. 2008. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using LiDAR data. Canadian Journal of Remote Sensing, 34:S338–S350.CrossRefGoogle Scholar
Falkowski, M.J., Smith, A.M.S., Hudak, A.T., Gessler, P.E., Vierling, L.A., and Crookston, N.L. 2006. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data. Canadian Journal of Remote Sensing, 32:153–161.CrossRefGoogle Scholar
Falkowski, M.J., Wulder, M.A., White, J.C., and Gillis, M.D. 2009b. Supporting large-area, sample-based forest inventories with very high spatial resolution satellite imagery. Progress in Physical Geography, 33:403–423.CrossRefGoogle Scholar
Field, C.B., Behrenfeld, M.J., Randerson, J.T., and Falkowski, P. 1998. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science, 281:237–240.CrossRefGoogle ScholarPubMed
Franklin, S.E., and Wulder, M.A. 2002. Remote sensing methods in medium spatial resolution satellite data land cover classifications of large areas. Progress in Physical Geography, 26:173–205.CrossRefGoogle Scholar
Franklin, S.E., Wulder, M.A., and Gerylo, G.R. 2001. Texture analysis of IKONOS panchromatic data for Douglas fir forest age class separability in British Columbia. International Journal of Remote Sensing, 22:2627–2632.CrossRefGoogle Scholar
French, N.H.F., de Groot, W.J., Jenkins, L.K., Rogers, B.M., Alvarado, E.C., Amiro, B.,…Turetsky, M. 2011. Model comparisons for estimating carbon emissions from North American wildland fire. Journal of Geophysical Research, 116:G00K05, .CrossRefGoogle Scholar
French, N.H.F., Kasischke, E.S., Bourgeau-Chavez, L.L., and Harrell, P.A. 1996. Sensitivity of ERS-1 SAR to variations in soil water in fire-disturbed boreal forest ecosystems. International Journal of Remote Sensing, 17:3037–3053.CrossRefGoogle Scholar
French, N.H.F., Kasischke, E.S., Hall, R.J., Murphy, K.A., Verbyla, D.L., Hoy, E.E., and Allen, J.L. 2008. Using Landsat data to assess fire and burn severity in the North American boreal forest region: An overview and summary of results. International Journal of Wildland Fire, 17:443–462, .CrossRefGoogle Scholar
Gamon, J.A., Penuelas, J., and Field, C.B. 1992. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment, 41:35–44.CrossRefGoogle Scholar
Garbulsky, M.F., Peñuelas, J., Gamon, J., Inoue, Y., and Filella, I. 2011. The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: A review and meta-analysis. Remote Sensing of Environment, 115:281–297.CrossRefGoogle Scholar
Garver, S.A., and Siegel, D.A. 1997. Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation. 1. Time series from the Sargasso Sea. Journal of Geophysical Research, 102:18607–18625.CrossRefGoogle Scholar
Gitelson, A.A., Gurlin, D., Moses, W.J., and Barrow, T. 2009. A bio-optical algorithm for the remote estimation of the chlorophyll-a concentration in case 2 waters. Environmental Research Letters, 4:045003, .CrossRefGoogle Scholar
Gitelson, A.A., Kaufman, Y.J., Stark, R., and Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80:76–87, .CrossRefGoogle Scholar
Goetz, S. 2007. Crisis in Earth observation. Science, 315:1767.CrossRefGoogle ScholarPubMed
Goetz, S. 2011. The lost promise of DESDynI. Remote Sensing of Environment, 115:2751, .CrossRefGoogle Scholar
Goetz, S.J., Baccini, A., Laport, N.T., Johns, T., Walker, W., Kellndorfer, J.,…Sun, M. 2009. Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balance and Management, 4:2, .CrossRefGoogle ScholarPubMed
Goetz, S.J., and Dubayah, R.O. 2011. Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change. Carbon Management, 2:231–244.CrossRefGoogle Scholar
Goetz, S.J., and Prince, S.D. 1999. Modeling terrestrial carbon exchange and storage: Evidence and implications of functional convergence in light use efficiency. Advances in Ecological Research, 28:57–92.CrossRefGoogle Scholar
Goetz, S.J., Prince, S.D., Goward, S.N., Thawley, M.M., and Small, J. 1999. Satellite remote sensing of primary production: An improved production efficiency modeling approach. Ecological Modelling, 122:239–255.CrossRefGoogle Scholar
Goetz, S.J., Sun, M., Baccini, A., and Beck, P.S.A. 2010. Synergistic use of space-borne LiDAR and optical imagery for assessing forest disturbance: An Alaska case study. Journal of Geophysical Research, 115:G00E07, .CrossRefGoogle Scholar
Gomez, C., Roseel, R.A.V., and McBratney, A.B. 2008. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma, 146:403–411.CrossRefGoogle Scholar
Gordon, H.R. 1994. Modeling and simulating radiative transfer in the ocean. In Ocean optics, ed. Spinrad, R., Carder, K., and Perry, M.J.. Oxford: Oxford University Press, pp. 3–39.Google Scholar
Gordon, H.R., and Morel, A. 1983. Remote assessment of ocean color for interpretation of satellite visible imagery. A review. New York: Springer-Verlag.CrossRefGoogle Scholar
Greenberg, J.A., Dobrowski, S.Z., and Ustin, S.L. 2005. Shadow allometry: Estimating tree structural parameters using hyperspatial image analysis. Remote Sensing of Environment, 97:15–25.CrossRefGoogle Scholar
Grenier, M., Demers, A.-M., Labrecque, S., Benoit, M., Fournier, R.A., and Drolet, B. 2007. An object-based method to map wetland using RADARSAT-1 and Landsat-ETM images: Test case on two sites in Quebec, Canada. Canadian Journal of Remote Sensing, 33:528–545.CrossRefGoogle Scholar
Grosse, G., Harden, J., Turetsky, M., McGuire, A.D., Camill, P., Tarnocai, C.,…Striegl, R.G. 2011. Vulnerability of high latitude soil organic carbon in North America to disturbance. Journal of Geophysical Research, 116:G00K06, .CrossRefGoogle Scholar
Grosse, G., Schirrmeister, L., Kunitsky, V.V., and Hubberten, H.W. 2005. The use of CORONA images in remote sensing of periglacial geomorphology: An illustration from the NE Siberian Coast. Permafrost and Periglacial Processes, 16:163–172.CrossRefGoogle Scholar
Hain, C.R., Mecikalski, J.R., and Anderson, M.C. 2009. Retrieval of an available water-based soil moisture proxy from thermal infrared remote sensing. Part I: Methodology and validation. Journal of Hydrometeorology, 10:665–683, .CrossRefGoogle Scholar
Hall, F.G., and Badhwar, G.D. 1987. Signature-extendable technology: Global space-based crop recognition. IEEE Transactions on Geoscience and Remote Sensing, GE-25:93–103.CrossRefGoogle Scholar
Hall, F.G., Hilker, T., and Coops, N.C. 2011. PHOTOSYNSAT, photosynthesis from space: Theoretical foundations of a satellite concept and validation from tower and spaceborne data. Remote Sensing of Environment, 115:1918–1925, .CrossRefGoogle Scholar
Hall, R.J. 2003. The roles of aerial photographs in forestry remote sensing image analysis. In Remote sensing of forest environments: Concepts and case studies, ed. Wulder, M.A. and Franklin, S.A.. Dordrecht: Kluwer, pp. 47–76.CrossRefGoogle Scholar
Hansen, M.C., Stehman, S.V., Potapov, P.V., Loveland, T.R., Townsend, J.R.G., DeFries, R.S.,…DiMiceli, C. 2008. Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data. Proceedings of the National Academy of Sciences, 105:9439–9444.CrossRefGoogle ScholarPubMed
Harmon, M.E., Bond-Lamberty, B., Tang, J., and Vargas, R. 2011. Heterotrophic respiration in disturbed forests: A review with examples from North America. Journal of Geophysical Research, 116:G00K04, .CrossRefGoogle Scholar
Harrell, P.A., Bourgeau-Chavez, L.L., Kasischke, E.S., French, N.H.F., and Christensen, N.L. 1995. Sensitivity of ERS-1 and JERS-1 radar data to biomass and stand structure in Alaskan boreal forest. Remote Sensing of Environment, 54:247–260.CrossRefGoogle Scholar
Harrell, P.A., Kasischke, E.S., Bourgeau-Chavez, L.L., Haney, E., and Christensen, N.L. 1997. Evaluation of approaches to estimating aboveground biomass in southern pine forests using SIR-C data. Remote Sensing of Environment, 59:223–233.CrossRefGoogle Scholar
Henderson, F.M., and Lewis, A.J. 1998. Principles and applications of imaging radar: Manual of remote sensing, 3d ed., vol. 2. New York: John Wiley and Sons.Google Scholar
Henderson, F.M., and Lewis, A.J. 2008. Radar detection of wetland ecosystems: A review. International Journal of Remote Sensing, 29:5809–5835.CrossRefGoogle Scholar
Herdendorf, C.E. 1982. Large lakes of the world. Journal of Great Lakes Research, 8:379–412.CrossRefGoogle Scholar
Hess, L.L., Melack, J.M., Filoso, S., and Wang, Y. 1995. IEEE Transactions on Geoscience and Remote Sensing, 33:896–904.CrossRef
Hicke, J.A., and Logan, J. 2009. Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery. International Journal of Remote Sensing, 30:4427–4441.CrossRefGoogle Scholar
Hilker, T., Coops, N.C., Wulder, M.A., Black, T.A., and Guy, R.D. 2008. The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements. Science of the Total Environment, 404:411–423.CrossRefGoogle ScholarPubMed
Hinton, M., Schiff, S., and English, M. 1998. Sources and flowpaths of dissolved organic carbon during storms in two forested watersheds of the Precambrian Shield. Biogeochemistry, 41:175–197.CrossRefGoogle Scholar
Hirano, A., Madden, M., and Welch, R. 2009. Hyperspectral image data for mapping wetland vegetation. Wetlands, 23:436–448.CrossRefGoogle Scholar
Hodgson, M.E., and Bresnahan, P. 2004. Accuracy of airborne lidar-derived elevation: Empirical assessment and error budget. Photogrammetric Engineering and Remote Sensing, 70:331–340.CrossRefGoogle Scholar
Hoge, F.E., and Lyon, P.E. 1996. Satellite retrieval of inherent optical properties by linear matrix inversion of ocean radiance models: An analysis of model and radiance measurement errors. Journal of Geophysical Research, 101:16631–16648.CrossRefGoogle Scholar
Houghton, R.A. 2008. Carbon flux to the atmosphere from land-use changes: 1850–2005. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tennessee.Google Scholar
Houghton, R.A., and Goetz, S.J. 2008. New satellites help quantify carbon sources and sinks. Eos, Transactions, American Geophysical Union, 89:417–418, .CrossRefGoogle Scholar
Huemmrich, K.F. 1995. An analysis of remote sensing of absorbed photosynthetically active radiation in forest canopies. College Park: University of Maryland.Google Scholar
Huntington, T.G. 2003. Climate warming could reduce runoff significantly in New England. Agricultural and Forest Meteorology, 117:193–201.CrossRefGoogle Scholar
Jensen, J.R. 2005. Introductory digital image processing: A remote sensing perspective. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Johnstone, J.F., and Chapin, F.S. 2006. Effects of soil burn severity on post-fire tree recruitment in boreal forests. Ecosystems, 9:14–31.CrossRefGoogle Scholar
Jorgenson, M.T., Shur, Y.L., and Pullman, E.R. 2006. Abrupt increase in permafrost degradation in Arctic Alaska. Geophysical Research Letters, 33:L02503, .CrossRefGoogle Scholar
Justice, C.O., Townshend, J.G.R., Holben, B.N., and Tucker, C.J. 1985. Analysis of the phenology of global vegetation using meterological satellite data. International Journal of Remote Sensing, 6:1271–1281.CrossRefGoogle Scholar
Kasischke, E.S., Bourgeau-Chavez, L.L., and Christensen, N.L. 1994. Observations on the sensitivity of ERS-1 SAR imagery to changes in aboveground biomass in young loblolly pine forests. International Journal of Remote Sensing, 15:3–16.CrossRefGoogle Scholar
Kasischke, E.S., Bourgeau-Chavez, L.L., Christensen, N.L., and Dobson, M.C. 1991. The relationship between aboveground biomass and radar backscatter as observed on airborne SAR imagery. Third AIRSAR Workshop, Pasadena, California.
Kasischke, E.S., Loboda, T., Giglio, L., French, N.H.F., Hoy, E.E., de Jong, B., and Riaño, D. 2011a. Quantifying burned area from fires in North American forests: Implications for direct reduction of carbon stocks. Journal of Geophysical Research, 116:G04003, .CrossRefGoogle Scholar
Kasischke, E.S., Tanase, M.A., Bourgeau-Chavez, L.L., and Borr, M. 2011b. Soil moisture limitations on monitoring boreal forest regrowth using spaceborne L-band SAR data. Remote Sensing of Environment, 115:277–232, .CrossRefGoogle Scholar
Kirk, J.T.O. 1980. Spectral absorption properties of natural waters: Contribution of the soluble and particular fractions to light absorption in some inland waters in south-eastern Australia. Marine and Freshwater Research, 31:287–296.CrossRefGoogle Scholar
Klein Goldewijk, K. 2001. Estimating global land use change over the past 300 years: The HYDE database. Global Biogeochemical Cycles, 15:417–433.CrossRefGoogle Scholar
Krabill, W.B., Collins, J.G., Link, L.E., Swift, R.N., and Butler, M.L. 1984. Airborne laser topographic mapping results. Photogrammetric Engineering and Remote Sensing, 50:685–694.Google Scholar
Kreutzweiser, D., Hazlett, P., and Gunn, J. 2008. Logging impacts on the biogeochemistry of boreal forest soils and nutrient export to aquatic systems: A review. Environmental Reviews, 16:157–179, .CrossRefGoogle Scholar
Kurz, W.A., Dymond, C.C., Stinson, G., Rampley, G.J., Neilson, E.T., Carroll, A.L.,…Safranyik, L. 2008. Mountain pine beetle and forest carbon feedback to climate change. Nature, 452:987–990, .CrossRefGoogle ScholarPubMed
Lal, R., Kimble, J.M., Follett, R.F., and Cole, C.V. 1998. The potential of U.S. cropland to sequester carbon and mitigate the greenhouse effect. Chelsea, MI: Ann Arbor Press.Google Scholar
Lambin, E.F., and Ehrlich, D. 1995. Combining vegetation indices and surface temperature for land-cover mapping at broad spatial scales. International Journal of Remote Sensing, 16(3):573–579.CrossRefGoogle Scholar
Lang, M.W., Kasischke, E.S., Prince, S.D., and Pittman, K.W. 2008. Assessment of C-band synthetic aperture radar data for mapping and monitoring Coastal Plain forested wetlands in the Mid-Atlantic Region, U.S.A. Remote Sensing of Environment, 112:4120–4130, .CrossRefGoogle Scholar
Lantuit, H., and Pollard, W.H. 2008. Fifty years of coastal erosion and retrogressive thaw slump activity on Herschel Island, southern Beaufort Sea, Yukon Territory, Canada. Geomorphology, 95:84–102.CrossRefGoogle Scholar
Lee, J.-S., and Pottier, E. 2009. Polarimetric radar imaging: From basics to applications. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
Lee, Z.P., Carder, K., Peacock, T.G., Davis, C.O., and Mueller, J.L. 1996. Method to derive ocean absorption coefficients from remote-sensing reflectance. Applied Optics, 35:452–462.CrossRefGoogle ScholarPubMed
Lee, Z.P., Carder, K.L., and Arnone, R. 2002. Deriving inherent optical properties from water color: A multi-band quasi-analytic algorithm for optically deep waters. Applied Optics, 41:5755–5772.CrossRefGoogle Scholar
Leenhouts, B. 1998. Assessment of biomass burning in the conterminous United States. Conservation Ecology [online], 2(1):1. .CrossRefGoogle Scholar
Lefsky, M.A. 2010. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophysical Research Letters, 37: L15401, .CrossRefGoogle Scholar
Lefsky, M.A., Cohen, W.B., Acker, S.A., Spies, T.A., Parker, G.G., and Harding, D. 1999. Lidar remote sensing of biophysical properties and canopy structure of forest of Douglas-fir and western hemlock. Remote Sensing of Environment, 70:339–361.CrossRefGoogle Scholar
Lefsky, M.A., Harding, D.J., Keller, M., Cohen, W.B., Carabajal, C.C., Espirito-Snato, Del Bom,…Oliveira, R. 2005. Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32:L22S02.CrossRefGoogle Scholar
Le Toan, T., Beaudoin, A., Riom, J., and Guyon, D. 1992. Relating forest biomass to SAR data. IEEE Transactions on Geoscience and Remote Sensing, 30:403–411.CrossRefGoogle Scholar
Le Toan, T., Quegan, S., Woodward, I., Lomas, M., Delbart, N., and Picard, G. 2004. Relating radar remote sensing of biomass to modeling of forest carbon budgets. Climatic Change, 67:379–402.CrossRefGoogle Scholar
Leinwand, I.I.F., Theobald, D.M., Mitchell, J., and Knight, R.L. 2010). Landscape dynamics at the public-private interface: A case study in Colorado. Landscape and Urban Planning, 97(3):182–193.CrossRefGoogle Scholar
Levesque, J., and King, D.J. 2003. Spatial analysis of radiometric fractions from high-resolution multispectral imagery for modelling forest structure and health. Remote Sensing of Environment, 84:589–602.CrossRefGoogle Scholar
Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. 2004. Remote sensing and image interpretation. New York: John Wiley and Sons.Google Scholar
Lim, K., Treitz, P., Wulder, M., St-Onge, B., and Flood, M. 2003. LiDAR remote sensing of forest structure. Progress in Physical Geography, 27:88–106.CrossRefGoogle Scholar
Liu, L., Zhang, T., and Wahr, J. 2010. InSAR measurements of surface deformation over permafrost on the North Slope of Alaska. Journal of Geophysical Research, 115:F03023, .Google Scholar
Liu, S., Bond-Lamberty, B., Hicke, J.A., Vargas, R., Zhao, S., Chen, J.,…Oeding, J. 2011. Simulating the impacts of disturbances on forest carbon cycling in North America: Processes, data, models, and challenges. Journal of Geophysical Research, 116:G00K08, .CrossRefGoogle Scholar
Lopez, R.D., Heggem, D.T., Sutton, D., Ehli, T., Van Remortel, R., Evanson, E., and Bice, L. 2006. Using landscape metrics to develop indicators of Great Lakes coastal wetland condition. U.S. Environmental Protection Agency Report, Las Vegas, Nevada. .Google Scholar
Luus, K.A., Robinson, D.T., and Deadman, P.J. 2011. Representing ecological processes in agent-based models of land use and cover change. Journal of Land Use Science, iFirst, 1–24.
MacDonald, R.B., and Hall, F.G. 1980. Global crop forecasting. Science, 208:670–679.CrossRefGoogle ScholarPubMed
Maclean, G.A., and Krabill, W.B. 1986. Gross-merchantable timber volume estimation using an airborne LiDAR system. Canadian Journal of Remote Sensing, 12:7–18.CrossRefGoogle Scholar
Maritorena, S., Siegel, D.A., and Peterson, A. 2002. Optimization of a semi-analytical ocean color model for global scale applications. Applied Optics, 41:2705–2714.CrossRefGoogle Scholar
Mars, J.C., and Houseknecht, D.W. 2007. Quantitative remote sensing study indicates doubling of coastal erosion rate in past 50 yr along a segment of the Arctic coast of Alaska. Geology, 35:583–586, .CrossRefGoogle Scholar
Masek, J.G., Cohen, W.B., Leckie, D., Wulder, M., Vargas, R., de Jong, B.,…Smith, W.B. 2011. Recent rates of forest harvest and conversion in North America. Journal of Geophysical Research, 116:G00K03, .CrossRefGoogle Scholar
Mattsson, T., Kortelainen, P., and Raike, A. 2005. Export of DOM from boreal catchments: Impacts of land use cover and climate. Biogeochemistry, 76:373–394, .CrossRefGoogle Scholar
Maynard, J.J., O’Green, A.T., and Dahlgren, R.A. 2008. The role of constructed wetlands in sequestering eroded carbon in an agricultural landscape. Abstract. Eos, Transactions, American Geophysical Union, 89(53).Google Scholar
McCarty, G., Pachepsky, Y., and Ritchie, J. 2009. Impact of sedimentation on wetland carbon sequestration in an agricultural watershed. Journal of Environmental Quality, 38(2):804–813.CrossRefGoogle Scholar
McCarty, G.W., Reeves, J.B., Reeves, V.B., Follet, R.F., and Kimble, J.M. 2002. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Science Society of America Journal, 66:640–646.Google Scholar
McCarty, G.W., and Reeves, J.B. 2006. Comparison of near infrared and mid infrared diffuse reflectance spectroscopy for field-scale measurements of soil fertility parameters. Soil Science, 171:94–102.CrossRefGoogle Scholar
McGuire, A.D., Anderson, L.G., Christensen, T.R., Dallimore, S., Guo, L., Hayes, D.J.,…Roulet, N. 2009. Sensitivity of the carbon cycle in the Arctic to climate change. Ecological Monographs, 79:523–555.CrossRefGoogle Scholar
McGuire, A.D., Macdonald, R.W., Schuur, E.A.G., Harden, J.W., Kuhry, P., Hayes, D.J.,…Heimann, M. 2010. The carbon budget of the northern cryosphere region. Current Opinion in Environmental Sustainability, 2:231–236.CrossRefGoogle Scholar
McNairn, H., and Protz, R. 1993. Mapping corn residues cover on agricultural fields in Oxford County, Ontario using thematic mapper. Canadian Journal of Remote Sensing, 19:152–159.CrossRefGoogle Scholar
Melon, P., Martinez, J.M., Le Toan, T., and Ulander, L.M.H. 2001. Analysis of VHF SAR data over pine forest. IEEE Transactions on Geoscience and Remote Sensing, 39:2364–2372.CrossRefGoogle Scholar
Miller, J.D., and Thode, A.E. 2007. Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109:66–80.CrossRefGoogle Scholar
Mobley, C.D., Stramski, D., Bissett, W.P., and Boss, E. 2004. Optical modeling of ocean waters: Is the Case 1 – Case 2 classification still useful?Oceanography, 17(2):60–67, .CrossRefGoogle Scholar
Monteith, J.L. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9:747–766.CrossRefGoogle Scholar
Moore, T., Matos, L., and Roulet, N. 2003. Dynamics and chemistry of dissolved organic carbon in Precambrian Shield catchments and an impounded wetland. Canadian Journal of Fisheries and Aquatic Sciences, 60:612–623, .CrossRefGoogle Scholar
Morel, A., and Prier, L. 1977. Analysis of variations in ocean color. Limnology and Oceanography, 22:709–722.CrossRefGoogle Scholar
Moss, E.M., and Guth, P.L. 2010. Deriving vegetation height from LiDAR DSMS an DTMS: The problem of negative vegetation heights. ASPRS 2010 Annual Conference, San Diego, California.
Nagler, P.L., Daughtry, C.S.T., and Goward, S.N. 2000. Plant litter and soil reflectance. Remote Sensing of Environment, 71:207–215.CrossRefGoogle Scholar
Nagler, P.L., Inoue, Y., Glenn, E.P., Russ, A.L., and Daughtry, C.S.T. 2003. Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes. Remote Sensing of Environment, 87:310–325.CrossRefGoogle Scholar
Nelson, R. 2010. Model effects on GLAS-based regional estimates of forest biomass and carbon. International Journal of Remote Sensing, 31:1359–1372.CrossRefGoogle Scholar
Nelson, R., Krabill, W.B., and Maclean, G. 1984. Determining forest canopy characteristics using airborne laser data. Remote Sensing of Environment, 15:201–212.CrossRefGoogle Scholar
Nelson, R., Ranson, K.J., Sun, G., Kimes, D.S., and Montesano, P. 2009. Estimating Siberian timber volume using MODIS and ICESat/GLAS. Remote Sensing of Environment, 113:691–701, .CrossRefGoogle Scholar
Nelson, R., Short, A., and Valenti, M. 2004. Measuring biomass and carbon in Delaware using airborne profiling LiDAR. Scandinavian Journal of Forest Research, 19:500–511.CrossRefGoogle Scholar
Oldak, A., Jackson, T.J., Starks, P., and Elliott, R. 2003. Mapping near-surface soil moisture on regional scale using ERS-2 SAR data. International Journal of Remote Sensing, 24:4579–4598.CrossRefGoogle Scholar
Ozdemir, I. 2008. Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened QuickBird imagery in open Crimean juniper forests. International Journal of Remote Sensing, 29:5643–5655.CrossRefGoogle Scholar
Palubinskas, G., Lucas, R.M., Foody, G.M., and Curran, P.J. 1995. An evaluation of fuzzy and texture-based classification approaches for mapping regenerating tropical forest classes from Landsat-TM data. International Journal of Remote Sensing, 16(4):747–759.CrossRefGoogle Scholar
Papathanassiou, K., Tette, T., Zimmermann, R., and Cloude, S.R. 2001. Forest biomass estimation using polarimetric SAR interferometry. Proceedings of ASAR’01, Montreal, Canada, October 1–4, 2001.
Plug, L.J., Walls, C., and Scott, B.M. 2008. Tundra lake changes from 1978 to 2001 on the Tuktoyaktuk Peninsula, western Canadian Arctic. Geophysical Research Letters, 35:L03502, .CrossRefGoogle Scholar
Pope, K., Reimankova, E., Paris, J., and Woodruff, R. 1997. Detecting seasonal flooding cycles in marshes of the Yucatan peninsula with SIR-C polarimetric radar imagery. Remote Sensing of Environment, 59:157–166.CrossRefGoogle Scholar
Popescu, S., Wynne, R., and Nelson, R. 2003. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Canadian Journal of Remote Sensing, 29:564–577.CrossRefGoogle Scholar
Popescu, S.C. 2007. Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 31:646–655.CrossRefGoogle Scholar
Potter, C., Klooster, S., Myneni, R., Genovese, V., Tan, P., and Kumar, V. 2003. Continental scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–98. Global and Planetary Change, 39:201–213.CrossRefGoogle Scholar
Potter, C.S., Randerson, J.T., Field, C.B., Matson, P.A., Vitousek, P.M., Mooney, H.A., and Klooster, S.A. 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Biogeochemical Cycles, 7:811–824.CrossRefGoogle Scholar
Powell, S.L., Healey, S.P., Cohen, W.B., Kennedy, R.E., Moisen, G.G., Pierce, K.B., and Ohmann, J.L. 2010. Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches. Remote Sensing of Environment, 114:1053–1068.CrossRefGoogle Scholar
Pozdnyakov, D., and Grassl, H. 2003. Colour of inland and coastal waters. Chichester, UK: Springer-Praxis.Google Scholar
Pozdnyakov, D., Shuchman, R., Korosov, A., and Hatt, C. 2005. Operational algorithm for the retrieval of water quality in the Great Lakes. Remote Sensing of Environment, 97:352–370, .CrossRefGoogle Scholar
Prince, S.D. 1991a. A model of regional primary production for use with coarse resolution satellite data. International Journal of Remote Sensing, 12:1313–1330.CrossRefGoogle Scholar
Prince, S.D. 1991b. Satellite remote sensing of primary production: Comparison of results for Sahelian grasslands 1981–1988. International Journal of Remote Sensing, 12:1301–1312.CrossRefGoogle Scholar
Prince, S.D., and Goward, S.J. 1995. Global primary production: A remote sensing approach. Journal of Biogeography, 22:815–835.CrossRefGoogle Scholar
Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., and Sorooshian, S. 1994. A modified soil adjusted vegetation index. Remote Sensing of Environment, 48:119–126, .CrossRefGoogle Scholar
Qi, J., Marsett, R., Heilman, P., Biedenbender, S., Moran, M.S., Goodrich, D.C., and Weltz, M. 2002. RANGES improves satellite-based information and land cover assessments in Southwest United States. EOS, Transactions, American Geophysical Union, 83:601–606.CrossRefGoogle Scholar
Quegan, S., Le Toan, T., Yu, J., Ribbes, F., and Floury, N. 2000. Estimating temperate forest area with multitemporal SAR data. IEEE Transactions on Geoscience and Remote Sensing, 38:741–753.CrossRefGoogle Scholar
Ramsey, E. 1998. Radar remote sensing of wetlands. In Remote sensing change detection: Environmental monitoring methods and applications, ed. Lunetta, R.S. and Elvidge, C.. Chelsea, MI: Ann Arbor Press, pp. 211–243.Google Scholar
Randerson, J.T., Thompson, M.V., Conway, T.J., Fung, I.Y., and Field, C.B. 1997. The contributions of terrestrial sources and sinks to trends in the seasonal cycle of atmospheric carbon dioxide. Global Biogeochemical Cycles, 11:535–560.CrossRefGoogle Scholar
Rauste, J., and Hame, T. 1994. Radar-based forest biomass estimation. International Journal of Remote Sensing, 15:2797–2807.CrossRefGoogle Scholar
Rauste, Y. 2005. Multi-temporal JERS SAR data in boreal forest biomass mapping. Remote Sensing of Environment, 97:263–275, .CrossRefGoogle Scholar
Reeves, J., McCarty, G., and Mimmo, T. 2002. The potential of diffuse reflectance spectroscopy for the determination of carbon inventories in soils. Environmental Pollution, 116:S277–S284, .CrossRefGoogle ScholarPubMed
Rencz, A.N. 1999. Manual of remote sensing: Remote sensing for the earth sciences, 3d ed., ed. Ryerson, R.A.. New York: John Wiley and Sons.Google Scholar
Reutebuch, S.E., McGaughey, R.J., Andersen, H.-E., and Carson, W.W. 2003. Accuracy of a high-resolution LIDAR terrain model under a conifer forest canopy. Canadian Journal of Remote Sensing, 29:527–535.CrossRefGoogle Scholar
Richards, M.A. 2007. A beginner's guide to interferometric SAR concepts and signal processing. IEEE A&E Systems Magazine, 22:5–29.CrossRefGoogle Scholar
Richardson, A.J., and Wiegand, C.L. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43:1541–1552.Google Scholar
Rignot, E., Way, J.B., McDonald, K., Viereck, L., Williams, C., Adams, P.,…Shi, J. 1994. Monitoring of environmental conditions in taiga forests using ERS-1 SAR data. Remote Sensing of Environment, 49:145–154.CrossRefGoogle Scholar
Robinson, D.T. 2012. Land-cover fragmentation and configuration of ownership parcels in an exurban landscape. Urban Ecosystems, 15:53–69, .CrossRefGoogle Scholar
Robinson, D.T., and Brown, D.G. 2009. Evaluating the effects of land-use development policies on ex-urban forest cover: An integrated agent-based GIS approach. International Journal of Geographical Information Science, 23:1211–1232.CrossRefGoogle Scholar
Roesler, C.S., and Boss, E. 2003. Spectral beam attenuation coefficient retrieved from ocean color inversion. Geophysical Research Letters, 30:1468–1472, .CrossRefGoogle Scholar
Roesler, C.S., and Boss, E. 2008. In situ measurement of the inherent optical properties (IOPs) and potential for harmful algal bloom (HAB) detection and coastal ecosystem observations. In Real-time coastal observing systems for marine ecosystem dynamics and harmful algal blooms: Theory, instrumentation and modelling, ed. Babin, M., Roesler, C.S., and Cullen, J.. Paris: UNESCO, pp. 153–206.Google Scholar
Roesler, C.S., and Perry, M.J. 1995. In situ phytoplankton absorption, fluorescence emission, and particulate backscattering spectra determined from reflectance. Journal of Geophysical Research, 100:13279–13294.CrossRefGoogle Scholar
Roy, D., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., and Lindquist, E. 2008. Multi-temporal MODIS–Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment, 112(6):3112–3130.CrossRefGoogle Scholar
Ruimy, A., Jarvis, P., Baldocchi, D., and Saugier, B. 1995. CO2 fluxes over plant canopies and solar radiation: A review. Advances in Ecological Research, 26:1–51.CrossRefGoogle Scholar
Running, S.W., Ramakrishna, R.N., Heinsch, F.A., Maosheng, Z., Reeves, M., and Hashimoto, H. 2004. A continuous satellite-derived measure of global terrestrial primary production. BioScience, 54:547–560.CrossRefGoogle Scholar
Santoro, M., Askne, J., Smith, G., and Fransson, J.E.S. 2002. Stem volume retrieval in boreal forests from ERS-1/2 interferometry. Remote Sensing of Environment, 81:19–35.CrossRefGoogle Scholar
Schanda, E. 1986. Physical fundamentals of remote sensing. Berlin: Springer-Verlag.CrossRefGoogle Scholar
Schneider von Deimling, T., Meinshausen, M., Levermann, A., Huber, V., Frieler, K., Lawrence, D.M., and Brovkin, V. 2011. Estimating the permafrost-carbon feedback on global warming. Biogeosciences Discussions, 8:4727–4761, .CrossRefGoogle Scholar
Schuur, E.A.G., Bockheim, J., Canadell, J.G., Euskirchen, E., Field, C.B., Goryachkin, S.V.,…Zimov, S.A. 2008. Vulnerability of permafrost carbon to climate change: Implications for the global carbon cycle. BioScience, 58:701–714.CrossRefGoogle Scholar
Sellers, P.J. 1985. Canopy reflectance, photosynthesis and transpiration. International Journal of Remote Sensing, 6:1335–1372.CrossRefGoogle Scholar
Sellers, P.J. 1987. Canopy reflectance, photosynthesis and transpiration. II. The role of biophysics in the linearity of their interdependence. Remote Sensing of Environment, 21:143–183.CrossRefGoogle Scholar
Serbin, G., Daughtry, C.S.T., Hunt, E.R., Reeves, J.B., and Brown, D.J. 2009. Effects of soil composition and mineralogy on remote sensing of crop residue cover. Remote Sensing of Environment, 113:224–238.CrossRefGoogle Scholar
Shan, J., and Toth, C.K. 2008. Topographic laser ranging and scanning: Principles and processing. Boca Raton, FL: Taylor & Francis.CrossRefGoogle Scholar
Shimada, M., Rosenqvist, A., Watanabe, M., and Tadono, T. 2005. The polarimetric and interferometric potential of ALOS PALSAR. POLinSAR 2005, Frascati, Italy, January 17–21, 2005.
Shuchman, R., Korosov, A., Hatt, C., Pozdnyakov, D., Means, J., and Meadows, G. 2006. Verification and application of a bio-optical algorithm for Lake Michigan using SeaWiFS: A 7-year inter-annual analysis. Journal of Great Lakes Research, 32:258–279.CrossRefGoogle Scholar
Skole, D., and Tucker, C. 1993. Tropical deforestation and habitat fragmentation in the Amazon: Satellite data from 1978 to 1988. Science, 260:1905–1909.CrossRefGoogle ScholarPubMed
Smith, G., Dammert, P.B.G., Santoro, M., Fransson, J.E.S., Wegmüller, U., and Askne, J.I.H. 1999. Biomass retrieval in boreal forest using ERS and JERS SAR. Proceedings of the 2nd International Workshop on Retrieval of Bio- & Geophysical Parameters from SAR Data for Land Applications, ESTEC, Noordwijk, The Netherlands, October 21–23, 1998.
Smith, L.C., Sheng, Y., MacDonald, G.M., and Hinzman, L.D. 2005. Disappearing arctic lakes. Science, 308:1429.CrossRefGoogle ScholarPubMed
Spencer, R., Aiken, G., Butler, K., Dornblaser, M., Striegl, R., and Hernes, P. 2009. Utilizing chromophoric dissolved organic matter measurements to derive export and reactivity of dissolved organic carbon exported to the Arctic Ocean: A case study of the Yukon River, Alaska. Geophysical Research Letters, 34:L12402, .Google Scholar
Stevens, A., Wesemael, B.V., Bartholomeus, H., Rosillon, D., Tychon, B., and Ben-Dor, E. 2008. Laboratory, field, and airborne spectroscopy for monitoring organic carbon content in agricultural soils. Geoderma, 144:395–404.CrossRefGoogle Scholar
Stevens, A., Wesemael, B.V., Vanderschrick, G., Touré, S., and Tychon, B. 2006. Detection of carbon stock change in agricultural soils using spectroscopic techniques. Soil Science Society of America Journal, 70(3):844–850.CrossRefGoogle Scholar
Stow, D., Lopez, A., Lippit, C., Hinton, S., and Weeks, J. 2007. Object-based classification of residential land use within Accra, Ghana based on QuickBird satellite data. International Journal of Remote Sensing, 28(22):5167–5173.CrossRefGoogle ScholarPubMed
Stramska, M., and Stramski, D. 2005. Variability of particulate organic carbon concentration in the north polar Atlantic based on ocean color observations with Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Journal of Geophysical Research, 110:C10018, .CrossRefGoogle Scholar
Stramski, D., Reynolds, R.A., Kahru, M., and Mitchell, B.G. 1999. Estimation of particulate organic carbon in the ocean from satellite remote sensing. Science, 285:239–242.CrossRefGoogle ScholarPubMed
Sun, G., Ranson, K.J., Kimes, D.S., Blair, J.B., and Kovacs, K. 2008. Forest vertical structure from GLAS: An evaluation using LVIS and SRTM data. Remote Sensing of Environment, 112:107–117.CrossRefGoogle Scholar
Tatem, A.J., Goetz, S.J., and Hay, S.I. 2008. Fifty years of earth observation satellites. American Scientist, 96:390–398.CrossRefGoogle ScholarPubMed
Townsend, P. 2002. Relationships between forest structure and the detection of flood inundation in forested wetlands using C-band SAR. International Journal of Remote Sensing, 22:443–460.CrossRefGoogle Scholar
Tucker, C.J. 1979. Red and photographic infrared linear combinations monitoring vegetation. Remote Sensing of Environment, 8:127–150.CrossRefGoogle Scholar
Tucker, C.J., and Sellers, P.J. 1986. Satellite remote sensing of primary production. International Journal of Remote Sensing, 7:1395–1416.CrossRefGoogle Scholar
Tucker, C.J., Vanpraet, C.L., Sharman, M.J., and Ittersum, G.V. 1985. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984. Remote Sensing of Environment, 17:233–249.CrossRefGoogle Scholar
Turetsky, M.R., Amiro, B.D., Bosch, E., and Bhatti, J.S. 2004. Historical burn area in western Canadian peatlands and its relationship to fire weather indices. Global Biogeochemical Cycles, 18:GB4014, .CrossRefGoogle Scholar
Ulrich, M., Grosse, G., Chabrillat, S., and Schirrmeister, L. 2009. Spectral characterization of periglacial surfaces and geomorphological units in the Arctic Lena Delta using field spectrometry and remote sensing. Remote Sensing of Environment, 113:1220–1235.CrossRefGoogle Scholar
van der Werf, G.R., Randerson, J.T., Giglio, L., Collatz, G.J., Mu, M., Kasibhatla, P.S.,…van Leeuwen, T.T. 2010. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmospheric Chemistry and Physics, 10:11707–11735, .CrossRefGoogle Scholar
van Deventer, A.P., Ward, A.D., Gowda, P.H., and Lyon, J.G. 1997. Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogrammetric Engineering and Remote Sensing, 63:87–93.Google Scholar
Verbyla, D., Kasischke, E., and Hoy, E. 2008. Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM +data. International Journal of Wildland Fire, 17:527–534, .CrossRefGoogle Scholar
Walter, K.M., Engram, M., Duguay, C.R., Jeffries, M.O., and Chapin, F.S. III. 2008. The potential use of synthetic aperture radar for estimating methane ebullition from arctic lakes. Journal of the American Water Resources Association, 44:305–315.CrossRefGoogle Scholar
Walter, K.M., Zimov, S.A., Chanton, J.P., Verbyla, D., and Chapin, F.S. III. 2006. Methane bubbling from Siberian thaw lakes as a positive feedback to climate warming. Nature, 443:71–75.CrossRefGoogle ScholarPubMed
Wear, D.N., and Bolstad, P. 1998. Land-use changes in Southern Appalachian landscapes: Spatial analysis and forecast evaluation. Ecosystems, 1(6):575–594.CrossRefGoogle Scholar
Weng, Q. 2011. Advances in environmental remote sensing: Sensors, algorithms, and applications. Boca Raton, FL: CRC Press.CrossRefGoogle Scholar
Whalen, S.C. 2005. Biogeochemistry of methane exchange between natural wetlands and the atmosphere. Environmental Engineering Science, 22:73–94, .CrossRefGoogle Scholar
Whitcomb, J., Moghaddam, M., McDonald, K., Kellndorfer, J., and Podest, E. 2009. Mapping vegetated wetlands of Alaska using L-band radar satellite imagery. Canadian Journal of Remote Sensing, 35:54–72.CrossRefGoogle Scholar
Whittaker, R.H., and Likens, G.E. 1973. Carbon in the biota. In Carbon and the biosphere, ed. Woodwell, G.M. and Pecan, E.V.. Springfield, VA: U.S. Atomic Energy Commission, pp. 281–302.Google Scholar
Woodcock, C.E., and Strahler, A.H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21:311–332, .CrossRefGoogle Scholar
Wulder, M.A., Bater, C.W., Coops, N.C., Hilker, T., and White, J.C. 2009. The role of LiDAR in sustainable forest management. Forestry Chronicle, 8(6):807–826.Google Scholar
Wulder, M.A., Dymond, C.C., White, J.C., Leckie, D.G., and Carroll, A.L. 2006. Surveying mountain pine beetle damage of forests: A review of remote sensing opportunities. Forest Ecology and Management, 221:27–41.CrossRefGoogle Scholar
Wulder, M.A., Hall, R.J., Coops, N.C., and Franklin, S.E. 2004. High spatial resolution remotely sensed data for ecosystem characterization. BioScience, 54:511–521.CrossRefGoogle Scholar
Yadav, V., and Malanson, G. 2007. Progress in soil organic matter research: Litter decomposition, modeling, monitoring and sequestration. Progress in Physical Geography, 2:131–154.CrossRefGoogle Scholar
Zaneveld, J.R.V. 1995. A theoretical derivation of the dependence of the remotely sensed reflectance of the ocean on the inherent optical properties. Journal of Geophysical Research, 100:13135–13142, .CrossRefGoogle Scholar

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