Characterization of Forest Crops with a Range of Nutrient and Water Treatments Using AISA Hyperspectral Imagery

References

• Albaugh, T. J., Allen, H. L., Dougherty, P. M., Kress, L. W., and J. S. King, 1998, "Leaf Area and Above- and Belowground Growth Responses of Loblolly Pine to Nutrient and Water Additions," Forest Science , 44:317-328.
   Google Scholar
• Anaya J. A., Chuvieco, E., and A. Palacios-Orueta, 2009, "Above-Ground Biomass Assessment in Colombia: A Remote Sensing Approach," Forest Ecology and Management , 257(4):1237-1246.
   Google Scholar
• ASD (Analytical Spectral Devices), 2005, Technical Guide (4th ed.), Boulder, CO: Analytical Spectral Devices.
   Google Scholar
• Bannari, A., Morin, D., Bonn, F., and A. R. Huete, 1995, "A Review of Vegetation Indices," Remote Sensing of Environment , 35:95-120.
   Google Scholar
• Bacour, C., Baret, F., Beal, D., Weiss, M., and K. Pavageau, 2006, "Neural Network Estimation of LAI, fAPAR, fCover, and LAIxC(ab) from Top of Canopy MERIS Reflectance Data: Principles and Validation," Remote Sensing of Environment , 105:313-325.
   Google Scholar
• Baret, F. and G. Guyot, 1991, "Potentials and Limits of Vegetation Indices for LAI and APAR Assessment," Remote Sensing of Environment , 35:161-173.
   Google Scholar
• Beeri, O., Phillips, R., Hendrickson, J., Frank, A. B., and S. Kronberg, 2007, "Estimating Forage Quantity and Quality Using Aerial Hyperspectral Imagery for Northern Mixed-Grass Prairie," Remote Sensing of Environment , 110:216-225.
   Google Scholar
• Borders, B. E. and R. L. Bailey, 2001, "Loblolly Pine—Pushing the Limits of Growth," Southern Journal of Applied Forestry , 25:69-74.
   Google Scholar
• Broge, N. H. and E. Leblanc, 2000, "Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density," Remote Sensing of Environment , 76:156-172.
   Google Scholar
• Chauve, A., Vega, C., Durrieu, S., Bretar, F., Allouis, T., Deseilligny, M. P., and W. Puech, 2009, "Advanced Full-Waveform Lidar Data Echo Detection: Assessing Quality of Derived Terrain and Tree Height Models in an Alpine Coniferous Forest," International Journal of Remote Sensing , 30(19):5211-5228.
   Google Scholar
• Chen, F., Weber, K. T., and B. Gokhale, 2011, "Herbaceous Biomass Estimation from SPOT 5 Imagery in Semiarid Rangelands of Idaho," GIScience and Remote Sensing , 48:195-209.
   Google Scholar
• Cho, M. A. and A. K. Skidmore, 2006, "A New Technique for Extracting the Red Edge Position from Hyperspectral Data: The Linear Extrapolation Method," Remote Sensing of Environment , 101:181-193.
   Google Scholar
• Cho, M. A., Skidmore, A., Corsi, F., Van Wieren, S. E., and I. Sobhan, 2007, "Estimation of Green Grass/Herb Biomass from Airborne Hyperspectral Imagery Using Spectral Indices and Partial Least Squares Regression," International Journal of Applied Earth Observation and Geoinformation , 9:414-424.
   Google Scholar
• Clevers, J., Jong, S., Epema, G., Meer, F., Bakker, W., Skidmore, A., and E. Addink, 2001, "MERIS and the Red-Edge Position," International Journal of Applied Earth Observation and Geoinformation , 3(4):313-320.
   Google Scholar
• Coleman, M. D., Coyle, D. R., Blake, J., Britton, K., Buford, M., Campbell, R. G., Cox, J., et al., 2004, Production of Short-Rotation Woody Crops Grown with a Range of Nutrient and Water Availability: Establishment Report and First-year Responses. Washington, DC: US Department of Agriculture, Technical report, SRS-72 Forest Service.
   Google Scholar
• Coyle, D. R. and M. D. Coleman, 2005, "Forest Production Responses to Irrigation and Fertilization Are Not Explained by Shifts in Allocation," Forest Ecology and Management , 208:137-152
   Google Scholar
• Coyle, D. R., Coleman, M. D., and D. P. Aubrey, 2008, "Above- and Below-Ground Biomass Accumulation, Production, and Distribution of Sweetgum and Loblolly Pine Grown with Irrigation and Fertilization," Canadian Journal of Forest Research , 38:1335-1348.
   Google Scholar
• Colombo, R., Meroni, M., Marchesi, A., Busetto, L., Rossini, M., Giardino, C., and C. Panigada, 2008, "Estimation of Leaf and Canopy Water Content in Poplar Plantations by Means of Hyperspectral Indices and Inverse Modeling," Remote Sensing of Environment , 112:1820-1834.
   Google Scholar
• Curran, P. J., 1989, "Remote Sensing of Foliar Chemistry," Remote Sensing of Environment , 30:271-278.
   Google Scholar
• Dawson, T. P. and P. J. Curran, 1998, "A New Technique for Interpolating the Reflectance Red Edge Position," International Journal of Remote Sensing , 19(11):2133-2139.
   Google Scholar
• De Jong, S. M., Pebesma, E. J., and B. Lacaze, 2003, "Above-Ground Biomass Assessment of Mediterranean Forests using Airborne Imaging Spectrometry: The DAIS Peyne Experiment," International Journal of Remote Sensing , 24(7):1505-1520.
   Google Scholar
• Elvidge, C. D. and R. J. P. Lyon, 1985, "Influence of Rock-Soil Spectral Variation on the Assessment of Green Biomass," Remote Sensing of Environment , 17:265-269.
   Google Scholar
• Gallaun, H., Zanchi, G., Nabuurs, G., Hengeveld, G., Schardt, M., and P. J. Verkerk, 2010, "EU-Wide Maps of Growing Stock and Above-Ground Biomass in Forests Based on Remote Sensing and Field Measurements," Forest Ecology and Management , 260(3):252-261.
   Google Scholar
• Gleason, C. and J. Im, 2011, "A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small Area Applications," GIScience and Remote Sensing , 48(2):141-170.
   Google Scholar
• Gleason, C. and J. Im, 2012a, "A Fusion Approach for Tree Crown Delineation from LiDAR Data," Photogrammetric Engineering & Remote Sensing , in press.
   Google Scholar
• Gleason, C. and J. Im, 2012b, "Forest Biomass Estimation from Airborne LiDAR Data Using Machine Learning Approaches," Remote Sensing of Environment , in press.
   Google Scholar
• Gong, P., Pu, R., and R. C. Heald, 2002, "Analysis of In Situ Hyperspectral Data for Nutrient Estimation of Giant Sequoia," International Journal of Remote Sensing , 23:1827-1850.
   Google Scholar
• Huete, A. R., Jackson, R. D., and D. F. Post, 1985, "Spectral Response of a Plant Canopy with Different Soil Backgrounds," Remote Sensing of Environment , 17:37-53.
   Google Scholar
• Hansen, P. M. and J. K. Schjoerring, 2003, "Reflectance Measurement of Canopy Biomass and Nitrogen Status in Wheat Crops Using Normalized Difference Vegetation Indices and Partial Least Squares Regression," Remote Sensing of Environment , 86:542-553.
   Google Scholar
• Horler, D. N. H., Dockray, M., and J. Barber, 1983, "The Red Edge of Plant Leaf Reflectance," International Journal of Remote Sensing , 4(2):273-288.
   Google Scholar
• Im, J. and J. R. Jensen, 2008, "Hyperspectral Remote Sensing of Vegetation," Geography Compass , 2(6):1943-1961.
   Google Scholar
• Im, J., Jensen J. R., Coleman, M., and E. Nelson, 2009, "Hyperspectral Remote Sensing Analysis of Short-Rotation Woody Crops Grown with Controlled Nutrient and Irrigation Treatment," Geocarto International , 24:293-312.
   Google Scholar
• Ioki K., Imanishi, J., Sasaki, T., Morimoto, Y., and K. Kitada, 2010, "Estimating Stand Volume in Broad-Leaved Forest Using Discrete-Return LiDAR: Plot-Based Approach," Landscape and Ecological Engineering , 6(1):29-36.
   Google Scholar
• Islam, A. S. and S. K. Bala, 2009, "Assessment of Potato Phenological Characteristics Using MODIS-Derived NDVI and LAI Information," GIScience and Remote Sensing , 45:454-470.
   Google Scholar
• Jacquemoud, S. and F. Baret, 1990, "PROSPECT: A Model of Leaf Optical Properties Spectra," Remote Sensing of Environment , 34:75-91.
   Google Scholar
• Jensen, J. R., 2007, Remote Sensing of the Environment: An Earth Resource Perspective , second ed., Upper Saddle River, NJ: Prentice Hall, 592 p.
   Google Scholar
• Jokela, E. J. and T. A. Martin, 2000, "Effects of Ontogeny and Soil Nutrient Supply on Production, Allocation, and Leaf Area Efficiency in Loblolly and Slash Pine Stands," Canadian Journal of Forest Research , 30:1511-1524.
   Google Scholar
• Knyazikhin, Y., Martonchik, J. V., Diner, D. J., Myneni, R. B., Verstraete, M., Pinty, B., et al., 1998, "Estimation of Vegetation Canopy Leaf Area Index and Fraction of Absorbed Photosynthetically Active Radiation from Atmosphere-Corrected MISR Data," Journal of Geophysical Research-Atmospheres , 103:32239-32256.
   Google Scholar
• Kozlowski, T. T., Kramer, P. J., and S. G. Pallardy, 1991, The Physiological Ecology of Woody Plants. San Diego, CA: Academic Press.
   Google Scholar
• Landsberg, J. J., 1986, Physiological Ecology of Forest Production. London, UK: Academic Press.
   Google Scholar
• Lee, K., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K., and S. T. Gower, 2004, "Hyperspectral versus Multispectral Data for Estimating Leaf Area Index in Four Different Biomes," Remote Sensing of Environment , 91:508-520.
   Google Scholar
• Lucas, R. M., Lee, A. C., and P. J. Bunting, 2008, "Retrieving Forest Biomass through Integration of CASI and LiDAR Data," International Journal of Remote Sensing , 29(5):1553-1577.
   Google Scholar
• Maire, G., Francois, C., Soudani, K., Berveiller, D., Pontailler, J., Breda, N., Genet, H., Davi, H., and E. Dufrene, 2008, "Calibration and Validation of Hyperspectral Indices for the Estimation of Broadleaved Forest Leaf Chlorophyll Content, Leaf Mass per Area, Leaf Area Index, and Leaf Canopy Biomass," Remote Sensing of Environment , 112:3846-3864.
   Google Scholar
• Marynard, C. L., Lawrence, R. L., Nielson, G. A., and G. Decker, 2007, "Modeling Vegetation Amount Using Bandwise Regression and Ecological Site Descriptions as an Alternative to Vegetation Indices," GIScience & Remote Sensing , 44(1):68-81.
   Google Scholar
• Masialeti, I., Egbert, S., and B. D. Wardlow, 2010, "A Comparative Analysis of Phenological Curves for Major Crops in Kansas," GIScience & Remote Sensing , 47(2):241-259.
   Google Scholar
• Popescu, S. C., 2007, "Estimating Biomass of Individual Pine Trees using Airborne Lidar," Biomass and Bioenergy , 31(9):646-655.
   Google Scholar
• Price, J. C., 1995, "Examples of High Resolution Visible to Near-Infrared Reflectance Spectra and a Standardized Collection for Remote Sensing Studies," International Journal of Remote Sensing , 16:993-1000.
   Google Scholar
• Riggins, J. J., Tullis, J. A., and F. M. Stephen, 2009, "Per-segment Aboveground Forest Biomass Estimation Using LiDAR-Derived Height Percentile Statistics," GIScience & Remote Sensing , 46(2):232-248.
   Google Scholar
• Salas, E. A. and G. M. Henebry, 2009, Area between Peaks Feature in the Derivative Reflectance Curve as a Sensitive Indicator of Change in Chlorophyll Concentration, GIScience and Remote Sensing , 46:315-328.
   Google Scholar
• Samuelson, L., Stokes, T., Cooksey, T., and P. McLemore, 2001, "Production Efficiency of Loblolly Pine and Sweetgum in Response to Four Years of Intensive Management," Tree Physiology , 21:369 - 376.
   Google Scholar
• Schlerf, M., Atzberger, C., and J. Hill, 2005, "Remote Sensing of Forest Biophysical Variables Using HyMap Imaging Spectrometer Data," Remote Sensing of Environment , 95:177-194.
   Google Scholar
• Schröder, J. J., Neeteson, J. J., Oenema, O., and P. C. Struik, 2000, "Does the Crop of the Soil Indicate How to Save Nitrogen in Maize Production?" Field Crops Research , 66:151-164.
   Google Scholar
• Smith, K. L., Steven, M. D., and J. J. Colls, 2004, "Use of Hyperspectral Derivative Ratios in the Red Edge Region to Identify Plant Stress Responses to Gas Leak," Remote Sensing of Environment , 92:207-217.
   Google Scholar
• Stroppiana, D., Boschetti, M., Brivio, P. A., and S. Bocchi, 2008, "Plant Nitrogen Concentration in Paddy Rice from Field Canopy Hyperspectral Radiometry," Field Crops Research , 111:119-129.
   Google Scholar
• Thenkabail, P. S., Smith, R. B., and E. D. Pauw, 2000, "Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics," Remote Sensing of Environment , 71:158-182.
   Google Scholar
• Trombetti, M., Riano, D., Rubio, M. A., Cheng, Y. B., and S. L. Ustin, 2008, "Multitemporal Vegetation Canopy Water Content Retrieval and Interpretation Using Artificial Neural Networks for the Continental USA," Remote Sensing of Environment , 112:203-215.
   Google Scholar
• Vaiphasa, C., Skidmore, A. K., Boer, W. F., and V. Tanasak, 2007, "A Hyperspectral Band Selector for Plant Species Discrimination," ISPRS Journal of Photogrammetry & Remote Sensing , 62:225-235.
   Google Scholar
• Verhoef, W., 1984, "Light Scattering by Leaf Layers with Application to Canopy Reflectance Modeling: The SAIL Model," Remote Sensing of Environment , 16:125-141.
   Google Scholar
• Verstraete, M., Pinty, B., and R. Myneni, 1996, Potential and Limitations of Information Extraction on the Terrestrial Biosphere from Satellite Remote Sensing," Remote Sensing of Environment , 58:201-214.
   Google Scholar
• Vos, J., van der Putten, P. E. L., and C. J. Birch, 2005, "Effect of Nitrogen Supply on Leaf Appearance, Leaf Growth, Leaf Nitrogen Economy, and Photosynthetic Capacity in Maize ( Zea mays L. )," Field Crops Research , 93:64-73.
   Google Scholar
• Wu, C., Niu, Z., Tang, Q., and W. Huang, 2008, "Estimating Chlorophyll Content from Hyperspectral Vegetation Indices: Modeling and Validation," Agricultural and Forest Meteorology , 148:1230-1241.
   Google Scholar
• Ye, X., Sakai, K., Okamoto, H., and L. O. Garciano, 2008, "A Ground-Based Hyperspectral Imaging System for Characterizing Vegetation Spectral Features," Computers and Electronics in Agriculture , 63:13-21.
   Google Scholar
• Zhao, D. H., Li, J. L., and J. G. Qi, 2005, "Identification of Red and NIR Spectral Regions and Vegetative Indices for Discrimination of Cotton Nitrogen Stress and Growth Stage," Computers and Electronics in Agriculture , 48:155-169.
   Google Scholar