Article, 2024

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

FRONTIERS IN REMOTE SENSING, Volume 4, 10.3389/frsen.2023.1188635

Contributors

Ojwang, Gordon O. (Corresponding author) [1] [2] Ogutu, Joseph O. [3] Said, Mohammed Y. [4] [5] Ojwala, Merceline A. [1] Kifugo, Shem C. [2] Verones, Francesca [5] Graae, Bente J. [5] Buitenwerf, Robert [6] Olff, Han [2]

Affiliations

  1. [1] Directorate Resource Surveys & Remote Sensing DRSR, Nairobi, Kenya
    2. [NORA names: Kenya; Africa];
  2. [2] Univ Groningen, Groningen Inst Evolutionary Life Sci, Groningen, Netherlands
    3. [NORA names: Netherlands; Europe, EU; OECD];
  3. [3] Univ Hohenheim, Inst Crop Sci, Biostat Unit, Stuttgart, Germany
    4. [NORA names: Germany; Europe, EU; OECD];
  4. [4] Univ Nairobi, Dept Earth & Climate Sci, Nairobi, Kenya
    5. [NORA names: Kenya; Africa];
  5. [5] Norwegian Univ Sci & Technol, Dept Biol, Trondheim, Norway
    6. [NORA names: Norway; Europe, Non-EU; Nordic; OECD];

Abstract

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Keywords

accuracy assessment, extended greater masai mara ecosystem (EGMME), heterogeneous socio-ecological systems, hierarchical classification, land use and land cover (LULC), landscape stratification, out-of-bag error, random forest

Data Provider: Clarivate

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