Regression-based Height Estimation

Regression-Based Height Estimation is a methodological approach within tree height estimation that employs statistical regression models to predict tree heights from covariates such as diameter at breast height, remote sensing data, or other measurable tree attributes, distinguishing itself by its reliance on empirical relationships derived from observed data.

81

Publications

3.6K

Citations

228

Authors

103

Institutions

Publications per year

2017–2026

13

Authors

228

  1. DW
    David W. Hann

    Oregon State University, United States

    6 publications · 156 citations

    6

    h-index

  2. HE
    Harold E. Burkhart

    Virginia Tech, United States

    3 publications · 192 citations

    3

    h-index

  3. ML
    Mark L. Hanus

    GTx (United States), United States

    3 publications · 55 citations

    3

    h-index

  4. DD
    David D. Marshall

    Pacific Northwest Research Station, United States

    3 publications · 55 citations

    3

    h-index

  5. TS
    T. Stijnen

    Erasmus University Rotterdam, Netherlands

    2 publications · 30 citations

    2

    h-index

1–5 of 228

Venues

  1. Canadian Journal of Forest Research

    8 publications · 741 citations

    8

    h-index

  2. PubMed

    Repository · 7 publications · 158 citations

    7

    h-index

  3. Forest Ecology and Management

    6 publications · 367 citations

    6

    h-index

  4. Forest Science

    5 publications · 347 citations

    5

    h-index

  5. Acta Paediatrica

    3 publications · 62 citations

    3

    h-index

1–5