Konferans bildirisi Açık Erişim

FOREST BIOPHYSICAL PARAMETER ESTIMATION VIA MACHINE LEARNING AND NEURAL NETWORK APPROACHES

Aksoy, Samet; Al Shwayyat, Shouq Zuhter Hasan; Topgul, Sule Nur; Sertel, Elif; Unsalan, Cem; Salo, Jari; Holmstrom, Anton; Wallerman, Jorgen; Nilsson, Mats; Fransson, Johan E. S.


DataCite XML

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/266976</identifier>
  <creators>
    <creator>
      <creatorName>Aksoy, Samet</creatorName>
      <givenName>Samet</givenName>
      <familyName>Aksoy</familyName>
      <affiliation>Istanbul Tech Univ, Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Al Shwayyat, Shouq Zuhter Hasan</creatorName>
      <givenName>Shouq Zuhter Hasan</givenName>
      <familyName>Al Shwayyat</familyName>
      <affiliation>Marmara Univ, Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Topgul, Sule Nur</creatorName>
      <givenName>Sule Nur</givenName>
      <familyName>Topgul</familyName>
      <affiliation>Istanbul Tech Univ, Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Sertel, Elif</creatorName>
      <givenName>Elif</givenName>
      <familyName>Sertel</familyName>
      <affiliation>Istanbul Tech Univ, Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Unsalan, Cem</creatorName>
      <givenName>Cem</givenName>
      <familyName>Unsalan</familyName>
      <affiliation>Marmara Univ, Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Salo, Jari</creatorName>
      <givenName>Jari</givenName>
      <familyName>Salo</familyName>
      <affiliation>Univ Helsinki, Helsinki, Finland</affiliation>
    </creator>
    <creator>
      <creatorName>Holmstrom, Anton</creatorName>
      <givenName>Anton</givenName>
      <familyName>Holmstrom</familyName>
      <affiliation>Katam Technol, Lund, Sweden</affiliation>
    </creator>
    <creator>
      <creatorName>Wallerman, Jorgen</creatorName>
      <givenName>Jorgen</givenName>
      <familyName>Wallerman</familyName>
      <affiliation>Swedish Univ Agr Sci, Uppsala, Sweden</affiliation>
    </creator>
    <creator>
      <creatorName>Nilsson, Mats</creatorName>
      <givenName>Mats</givenName>
      <familyName>Nilsson</familyName>
      <affiliation>Swedish Univ Agr Sci, Uppsala, Sweden</affiliation>
    </creator>
    <creator>
      <creatorName>Fransson, Johan E. S.</creatorName>
      <givenName>Johan E. S.</givenName>
      <familyName>Fransson</familyName>
      <affiliation>Linnaeus Univ, Vaxjo, Sweden</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Forest Biophysical Parameter Estimation Via Machine Learning And Neural Network Approaches</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <dates>
    <date dateType="Issued">2023-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/266976</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/IGARSS52108.2023.10282899</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This paper presents the first results of the ongoing development of new forest mapping methods for the Swedish national forest mapping case using Airborne Laser Scanning (ALS) data, utilizing the recent findings in machine learning (ML) and Artificial Intelligence (AI) techniques. We used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) as ML models. In addition, Neural networks (NN) based approaches were utilized in this study. ALS derived features were used to estimate the stem volume (V), above-ground biomass (AGB), basal area (B), tree height (H), stem diameter (D), and forest stand age (A). XGBoost ML algorithm outperformed RF 1 % to 3 % in the R-2 metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.&lt;/p&gt;</description>
  </descriptions>
</resource>
0
0
görüntülenme
indirilme
Görüntülenme 0
İndirme 0
Veri hacmi 0 Bytes
Tekil görüntülenme 0
Tekil indirme 0

Alıntı yap