Regression-based Basal Area Estimation

Regression-Based Basal Area Estimation is a statistical modeling approach within remote sensing-based forest inventory that employs regression techniques to predict basal area from ancillary variables, distinguishing itself by leveraging empirical relationships between field-measured basal area and predictor variables derived from remote sensing data.

34

Publications

2.3K

Citations

93

Authors

46

Institutions

Publications per year

2017–2026

3

Authors

93

  1. MM
    Matti Maltamo

    University of Eastern Finland, Finland

    4 publications · 185 citations

    4

    h-index

  2. TC
    Thomas C. Edwards

    Utah State University, United States

    1 publications · 280 citations

    1

    h-index

  3. PJ
    Patrick JB Walker

    1 publications · 49 citations

    1

    h-index

  4. TE
    Thomas E. Burk

    University of Minnesota, United States

    1 publications · 14 citations

    1

    h-index

  5. HE
    Harold E. Burkhart

    Virginia Tech, United States

    1 publications · 22 citations

    1

    h-index

1–5 of 93

Venues

  1. Forest Ecology and Management

    11 publications · 417 citations

    11

    h-index

  2. Forest Science

    3 publications · 504 citations

    3

    h-index

  3. Silva Fennica

    2 publications · 70 citations

    2

    h-index

  4. Canadian Journal of Forest Research

    2 publications · 142 citations

    2

    h-index

  5. Northern Journal of Applied Forestry

    1 publications · 88 citations

    1

    h-index

1–5