Estimating Tropical Forest Structure Using a Terrestrial Lidar
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{"title"=>"Estimating Tropical Forest Structure Using a Terrestrial Lidar", "type"=>"journal", "authors"=>[{"first_name"=>"Michael", "last_name"=>"Palace", "scopus_author_id"=>"6506443781"}, {"first_name"=>"Franklin B.", "last_name"=>"Sullivan", "scopus_author_id"=>"55889254600"}, {"first_name"=>"Mark", "last_name"=>"Ducey", "scopus_author_id"=>"7006125701"}, {"first_name"=>"Christina", "last_name"=>"Herrick", "scopus_author_id"=>"56562681400"}], "year"=>2016, "source"=>"PloS one", "identifiers"=>{"scopus"=>"2-s2.0-84988007805", "sgr"=>"84988007805", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0154115", "isbn"=>"e0154115", "pui"=>"614890496"}, "id"=>"2d7532da-3102-30c5-b850-fa4842c2ee21", "abstract"=>"<p>Forest structure comprises numerous quantifiable biometric components and characteristics, which include tree geometry and stand architecture. These structural components are important in the understanding of the past and future trajectories of these biomes. Tropical forests are often considered the most structurally complex and yet least understood of forested ecosystems. New technologies have provided novel avenues for quantifying biometric properties of forested ecosystems, one of which is LIght Detection And Ranging (lidar). This sensor can be deployed on satellite, aircraft, unmanned aerial vehicles, and terrestrial platforms. In this study we examined the efficacy of a terrestrial lidar scanner (TLS) system in a tropical forest to estimate forest structure. Our study was conducted in January 2012 at La Selva, Costa Rica at twenty locations in a predominantly undisturbed forest. At these locations we collected field measured biometric attributes using a variable plot design. We also collected TLS data from the center of each plot. Using this data we developed relative vegetation profiles (RVPs) and calculated a series of parameters including entropy, Fast Fourier Transform (FFT), number of layers and plant area index to develop statistical relationships with field data. We developed statistical models using a series of multiple linear regressions, all of which converged on significant relationships with the strongest relationship being for mean crown depth (<italic>r</italic><sup><italic>2</italic></sup> = 0.88, p &lt; 0.001, RMSE = 1.04 m). Tree density was found to have the poorest significant relationship (<italic>r</italic><sup><italic>2</italic></sup> = 0.50, <italic>p</italic> &lt; 0.01, <italic>RMSE</italic> = 153.28 n ha<sup>-1</sup>). We found a significant relationship between basal area and lidar metrics (<italic>r</italic><sup><italic>2</italic></sup> = 0.75, <italic>p</italic> &lt; 0.001, <italic>RMSE</italic> = 3.76 number ha<sup>-1</sup>). Parameters selected in our models varied, thus indicating the potential relevance of multiple features in canopy profiles and geometry that are related to field-measured structure. Models for biomass estimation included structural canopy variables in addition to height metrics. Our work indicates that vegetation profiles from TLS data can provide useful information on forest structure.</p>", "link"=>"http://www.mendeley.com/research/estimating-tropical-forest-structure-using-terrestrial-lidar-1", "reader_count"=>56, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Librarian"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>16, "Student > Bachelor"=>7}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Librarian"=>1, "Researcher"=>10, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>1, "Student > Master"=>16, "Student > Bachelor"=>7}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>2, "Environmental Science"=>20, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>15, "Physics and Astronomy"=>2, "Computer Science"=>1, "Earth and Planetary Sciences"=>14}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>14}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>20}}, "reader_count_by_country"=>{"Brazil"=>1}, "group_count"=>6}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/5047408"], "description"=>"<p>Vertical profile derived lidar metrics.</p>", "links"=>[], "tags"=>["Fast Fourier Transform", "RVP", "location", "quantifiable biometric components", "Terrestrial Lidar Forest structure", "r 2", "FFT", "series", "estimate forest structure", "plant area index", "relationship", "quantifying biometric properties", "canopy", "lidar", "forested ecosystems", "vegetation profiles", "Tropical Forest Structure", "RMSE", "TLS data", "model"], "article_id"=>3215791, "categories"=>["Biophysics", "Chemical Sciences not elsewhere classified", "Ecology", "Biological Sciences not elsewhere classified"], "users"=>["Michael Palace", "Franklin B Sullivan", "Mark Ducey", "Christina Herrick"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0154115.t003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Vertical_profile_derived_lidar_metrics_/3215791", "title"=>"Vertical profile derived lidar metrics.", "pos_in_sequence"=>6, "defined_type"=>3, "published_date"=>"2016-04-28 08:48:54"}

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  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}

Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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