Estimating Above-Ground Carbon Biomass in a Newly Restored Coastal Plain Wetland Using Remote Sensing
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{"title"=>"Estimating Above-Ground Carbon Biomass in a Newly Restored Coastal Plain Wetland Using Remote Sensing", "type"=>"journal", "authors"=>[{"first_name"=>"Joseph B.", "last_name"=>"Riegel", "scopus_author_id"=>"56050749100"}, {"first_name"=>"Emily", "last_name"=>"Bernhardt", "scopus_author_id"=>"7007044685"}, {"first_name"=>"Jennifer", "last_name"=>"Swenson", "scopus_author_id"=>"35793352600"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"369209161", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\n1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0068251", "scopus"=>"2-s2.0-84879511608", "pmid"=>"23840837", "sgr"=>"84879511608"}, "id"=>"d2dc68c2-fac7-39cf-8742-38f517e6a526", "abstract"=>"Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R2 values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R2 of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas. © 2013 Riegel et al.", "link"=>"http://www.mendeley.com/research/estimating-aboveground-carbon-biomass-newly-restored-coastal-plain-wetland-using-remote-sensing", "reader_count"=>47, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>6, "Researcher"=>3, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>8, "Other"=>1, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Student > Doctoral Student"=>6, "Researcher"=>3, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>8, "Other"=>1, "Student > Bachelor"=>7, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Environmental Science"=>15, "Agricultural and Biological Sciences"=>14, "Medicine and Dentistry"=>1, "Sports and Recreations"=>1, "Computer Science"=>1, "Earth and Planetary Sciences"=>10}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>14}, "Computer Science"=>{"Computer Science"=>1}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>15}}, "reader_count_by_country"=>{"Colombia"=>1, "Morocco"=>1, "Brazil"=>1, "India"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1105693"], "description"=>"<p>Vegetation measurements were collected annually from 10×10 m plots within a section of the wetland mitigation area sold to the NC Ecosystem Enhancement Program (NCEEP), a state in lieu fee trading program (red boxes). These NC EEP plots covered the northern part of the study area and included both riverine and non-riverine areas; however, there were no plots within the portions of the site planted with white cedar.</p>", "links"=>[], "tags"=>["Forestry", "ecology", "Ecological metrics", "Biomass (ecology)", "Biogeochemistry", "Conservation science", "Macroecology", "Restoration ecology", "Spatial and landscape ecology", "Systems ecology", "geochemistry", "Carbon cycle", "geography", "Cartography", "Remote sensing imagery", "locations", "vegetation"], "article_id"=>735114, "categories"=>["Medicine", "Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Joseph B. Riegel", "Emily Bernhardt", "Jennifer Swenson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068251.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Map_of_plot_locations_and_dominant_vegetation_types_/735114", "title"=>"Map of plot locations and dominant vegetation types.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-28 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1105695"], "description"=>"<p>The top left panel is a histogram of the sample above-ground carbon biomass data. The other panels are histograms of the plot biomass data predicted by the three models. Each of the three models had difficulty correctly capturing the observed distribution of AGCB at the study site.</p>", "links"=>[], "tags"=>["Forestry", "ecology", "Ecological metrics", "Biomass (ecology)", "Biogeochemistry", "Conservation science", "Macroecology", "Restoration ecology", "Spatial and landscape ecology", "Systems ecology", "geochemistry", "Carbon cycle", "geography", "Cartography", "Remote sensing imagery", "agcb", "modeled"], "article_id"=>735116, "categories"=>["Medicine", "Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Joseph B. Riegel", "Emily Bernhardt", "Jennifer Swenson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068251.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histograms_of_sample_AGCB_data_and_modeled_AGCB_estimates_/735116", "title"=>"Histograms of sample AGCB data and modeled AGCB estimates.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-28 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1105700"], "description"=>"<p>For the three models, the predicted above-ground carbon biomass values for the 76 EEP plots are graphed against the above-ground carbon biomass values estimated using field techniques. Points above the one-to-one line represent plots for which the model over-estimated AGCB. Points below the one-to-one line represent plot for which the model under-estimated AGCB. To varying degrees, all the models over-estimated low observed AGCB values and under-estimated high observed AGCB values.</p>", "links"=>[], "tags"=>["Forestry", "ecology", "Ecological metrics", "Biomass (ecology)", "Biogeochemistry", "Conservation science", "Macroecology", "Restoration ecology", "Spatial and landscape ecology", "Systems ecology", "geochemistry", "Carbon cycle", "geography", "Cartography", "Remote sensing imagery", "observed", "agcb", "sensing"], "article_id"=>735121, "categories"=>["Medicine", "Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Joseph B. Riegel", "Emily Bernhardt", "Jennifer Swenson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068251.g003", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphs_of_predicted_vs_observed_AGCB_for_the_three_remote_sensing_models_/735121", "title"=>"Graphs of predicted vs. observed AGCB for the three remote sensing models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-28 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1105705"], "description"=>"<p>These maps were created by applying the biomass models to the respective remote sensing datasets which covered the entire study area. Note the differences in overall carbon biomass density and differences in the distribution of biomass at the study site as predicted by the three models.</p>", "links"=>[], "tags"=>["Forestry", "ecology", "Ecological metrics", "Biomass (ecology)", "Biogeochemistry", "Conservation science", "Macroecology", "Restoration ecology", "Spatial and landscape ecology", "Systems ecology", "geochemistry", "Carbon cycle", "geography", "Cartography", "Remote sensing imagery", "agcb", "created", "sensing"], "article_id"=>735123, "categories"=>["Medicine", "Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Joseph B. Riegel", "Emily Bernhardt", "Jennifer Swenson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068251.g004", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maps_of_estimated_AGCB_created_using_the_three_remote_sensing_models_/735123", "title"=>"Maps of estimated AGCB created using the three remote sensing models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-28 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1105707"], "description"=>"<p>Among the 76 vegetation plots, V10 was the plot with the highest LiDAR pulse density (9.76/m<sup>2</sup>). The black dots represent the point returns. The dark gray areas around the black dots represent the LiDAR pulse footprints. Note the large areas (light gray) with no points which were effectively not sampled.</p>", "links"=>[], "tags"=>["Forestry", "ecology", "Ecological metrics", "Biomass (ecology)", "Biogeochemistry", "Conservation science", "Macroecology", "Restoration ecology", "Spatial and landscape ecology", "Systems ecology", "geochemistry", "Carbon cycle", "geography", "Cartography", "Remote sensing imagery", "vegetation", "v10", "lidar", "returns"], "article_id"=>735125, "categories"=>["Medicine", "Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Joseph B. Riegel", "Emily Bernhardt", "Jennifer Swenson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068251.g005", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Map_of_vegetation_plot_V10_showing_LiDAR_point_returns_and_pulse_footprints_/735125", "title"=>"Map of vegetation plot V10 showing LiDAR point returns and pulse footprints.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-28 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1105709"], "description"=>"<p>Descriptive statistics for model AGCB predictions over entire study area.</p>", "links"=>[], "tags"=>["Forestry", "ecology", "Ecological metrics", "Biomass (ecology)", "Biogeochemistry", "Conservation science", "Macroecology", "Restoration ecology", "Spatial and landscape ecology", "Systems ecology", "geochemistry", "Carbon cycle", "geography", "Cartography", "Remote sensing imagery", "agcb", "predictions"], "article_id"=>735127, "categories"=>["Medicine", "Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Joseph B. Riegel", "Emily Bernhardt", "Jennifer Swenson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068251.t006", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptive_statistics_for_model_AGCB_predictions_over_entire_study_area_/735127", "title"=>"Descriptive statistics for model AGCB predictions over entire study area.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-28 03:05:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/1105710"], "description"=>"<p>Descriptive statistics for sample carbon biomass data.</p>", "links"=>[], "tags"=>["Forestry", "ecology", "Ecological metrics", "Biomass (ecology)", "Biogeochemistry", "Conservation science", "Macroecology", "Restoration ecology", "Spatial and landscape ecology", "Systems ecology", "geochemistry", "Carbon cycle", "geography", "Cartography", "Remote sensing imagery", "carbon", "biomass"], "article_id"=>735128, "categories"=>["Medicine", "Biological Sciences", "Earth and Environmental Sciences"], "users"=>["Joseph B. Riegel", "Emily Bernhardt", "Jennifer Swenson"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0068251.t004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Descriptive_statistics_for_sample_carbon_biomass_data_/735128", "title"=>"Descriptive statistics for sample carbon biomass data.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-28 03:05:06"}
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  • {"unique-ip"=>"5", "full-text"=>"2", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"10"}
  • {"unique-ip"=>"2", "full-text"=>"2", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"6", "full-text"=>"6", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
  • {"unique-ip"=>"6", "full-text"=>"8", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"4", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"6", "full-text"=>"3", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"6", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Agriculture", "average_usage"=>[241, 422, 551, 659, 765, 866, 954, 1043, 1142, 1235, 1311, 1397, 1462]}, {"subject_area"=>"/Biology and life sciences/Ecology", "average_usage"=>[290, 478, 601, 716, 816, 914, 1016, 1112, 1203, 1285, 1373, 1451, 1516]}, {"subject_area"=>"/Earth sciences", "average_usage"=>[296, 488, 620, 717, 828, 938, 1038, 1130, 1230, 1328, 1414, 1502, 1592]}, {"subject_area"=>"/Earth sciences/Hydrology", "average_usage"=>[253, 387, 495, 587, 670, 757, 847, 930, 1011, 1082, 1167, 1237, 1309]}, {"subject_area"=>"/Ecology and environmental sciences", "average_usage"=>[284, 475, 603, 722, 826, 928, 1026, 1129, 1225, 1310, 1390, 1468, 1549]}, {"subject_area"=>"/Ecology and environmental sciences/Ecological environments", "average_usage"=>[261, 401]}, {"subject_area"=>"/Ecology and environmental sciences/Ecology", "average_usage"=>[298, 487, 610, 722, 827, 929, 1029, 1125, 1217, 1306, 1388, 1464, 1535]}, {"subject_area"=>"/Ecology and environmental sciences/Terrestrial environments", "average_usage"=>[250, 412, 522, 634, 716, 804, 892, 973, 1056, 1147, 1211, 1284, 1345]}, {"subject_area"=>"/Engineering and technology", "average_usage"=>[254, 440, 558, 675, 785, 883, 972, 1061, 1154, 1248, 1336, 1414, 1489]}]}
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