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@article{bb339707,
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@article{bb339708,
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@article{bb339709,
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@article{bb339711,
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@article{bb339752,
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Quality of Forage for Migratory Elk in the Greater Yellowstone
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Corresponding Solutions to Improve Forest Aboveground Biomass
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@article{bb339754,
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@article{bb339755,
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        TITLE = "Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon
Density by Combining Image and Plot Data from a Nested and Clustering
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@article{bb339756,
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@article{bb339757,
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@article{bb339758,
        AUTHOR = "Su, Y. and Zhang, W.F. and Liu, B.J. and Tian, X. and Chen, S.X. and Wang, H. and Mao, Y.W.",
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Incorporation of Remotely Sensed Model with Process-Based Model",
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        BIBSOURCE = "http://www.visionbib.com/bibliography/cartog951v1.html#TT334116"}

@article{bb339759,
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        TITLE = "Can Hawaii Meet Its Renewable Fuel Target? Case Study of
Banagrass-Based Cellulosic Ethanol",
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@article{bb339760,
        AUTHOR = "Messinger, M. and Asner, G.P. and Silman, M.",
        TITLE = "Rapid Assessments of Amazon Forest Structure and Biomass Using Small
Unmanned Aerial Systems",
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@article{bb339761,
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        TITLE = "Relasphone: Mobile and Participative In Situ Forest Biomass
Measurements Supporting Satellite Image Mapping",
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@article{bb339762,
        AUTHOR = "Adab, H. and Kanniah, K.D. and Beringer, J.",
        TITLE = "Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content
of a Temperate Humid Forest Using Multi Resolution Remote Sensing
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@article{bb339763,
        AUTHOR = "Wang, Q.A. and Pang, Y. and Li, Z.Y. and Sun, G.Q. and Chen, E. and Ni Meister, W.",
        TITLE = "The Potential of Forest Biomass Inversion Based on Vegetation Indices
Using Multi-Angle CHRIS/PROBA Data",
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@article{bb339764,
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        TITLE = "Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery
in a Tropical Woodland",
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@article{bb339765,
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        TITLE = "Estimating Aboveground Biomass in Tropical Forests: Field Methods and
Error Analysis for the Calibration of Remote Sensing Observations",
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        NUMBER = "1",
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@article{bb339766,
        AUTHOR = "Gwenzi, D. and Helmer, E.H. and Zhu, X.L. and Lefsky, M.A. and Marcano Vega, H.",
        TITLE = "Predictions of Tropical Forest Biomass and Biomass Growth Based on
Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology
across Puerto Rico and the U.S. Virgin Islands",
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@article{bb339767,
        AUTHOR = "Balenovic, I. and Milas, A.S. and Marjanovic, H.",
        TITLE = "A Comparison of Stand-Level Volume Estimates from Image-Based Canopy
Height Models of Different Spatial Resolutions",
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@article{bb339768,
        AUTHOR = "Zhu, J. and Huang, Z.H. and Sun, H. and Wang, G.X.",
        TITLE = "Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin
by Combining Plot and Remote Sensing Data and Comparing Spatial
Extrapolation Methods",
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        NUMBER = "3",
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@article{bb339769,
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        TITLE = "Inverting Aboveground Biomass-Canopy Texture Relationships in a
Landscape of Forest Mosaic in the Western Ghats of India Using Very
High Resolution Cartosat Imagery",
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@article{bb339770,
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        TITLE = "Biomass Estimation of Xerophytic Forests Using Visible Aerial
Imagery: Contrasting Single-Tree and Area-Based Approaches",
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@article{bb339771,
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above Ground Biomass Using TM and GLAS Data",
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@article{bb339772,
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Inventories",
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@article{bb339773,
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Inventories of Dry Tropical Forests Assisted by Photogrammetric Data
from an Unmanned Aircraft System",
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@article{bb339774,
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Fluxes in a Mediterranean Tree-Grass Ecosystem",
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@article{bb339776,
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@article{bb339777,
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@article{bb339778,
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@article{bb339779,
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@article{bb339780,
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@article{bb339781,
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@article{bb339783,
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@article{bb339786,
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@article{bb339787,
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Assessing the Potential to Reconstruct Canopy Structure and Estimate
Living Biomass",
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@article{bb339788,
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Opportunities for Remote Sensing Science",
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@article{bb339790,
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@article{bb339792,
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@article{bb339793,
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@article{bb339794,
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@article{bb339796,
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@article{bb339797,
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@article{bb339798,
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@article{bb339799,
        AUTHOR = "Fu, Y.Y. and He, H.S. and Hawbaker, T.J. and Henne, P.D. and Zhu, Z.L. and Larsen, D.R.",
        TITLE = "Evaluating k-Nearest Neighbor (kNN) Imputation Models for
Species-Level Aboveground Forest Biomass Mapping in Northeast China",
        JOURNAL = RS,
        VOLUME = "11",
        YEAR = "2019",
        NUMBER = "17",
        PAGES = "xx-yy",
        BIBSOURCE = "http://www.visionbib.com/bibliography/cartog951v1.html#TT334157"}

Last update:Nov 26, 2025 at 20:24:09