Stand Attribute Estimation

Stand Attribute Estimation is the process of deriving quantitative forest stand characteristics, such as biomass, height, density, or species composition, from remotely sensed data through statistical or machine learning models, distinguished from broader land cover classification by its focus on measuring specific biophysical and structural properties of forest stands at a defined spatial scale.

89

Publications

6.9K

Citations

286

Authors

153

Institutions

Publications per year

2017–2026

24

Authors

286

  1. MM
    Matti Maltamo

    University of Eastern Finland, Finland

    13 publications · 985 citations

    12

    h-index

  2. PP
    Petteri Packalén

    University of Eastern Finland, Finland

    5 publications · 645 citations

    5

    h-index

  3. HT
    Hailemariam Temesgen

    Oregon State University, United States

    4 publications · 185 citations

    4

    h-index

  4. JM
    Jukka Malinen

    University of Eastern Finland, Finland

    3 publications · 235 citations

    3

    h-index

  5. AG
    Alkan Günlü

    Çankırı Karatekin University, Türkiye

    3 publications · 49 citations

    3

    h-index

1–5 of 286

Venues

  1. Forest Ecology and Management

    11 publications · 1.2K citations

    11

    h-index

  2. Remote Sensing of Environment

    10 publications · 3K citations

    10

    h-index

  3. Canadian Journal of Forest Research

    9 publications · 563 citations

    9

    h-index

  4. International Journal of Remote Sensing

    7 publications · 488 citations

    7

    h-index

  5. Silva Fennica

    6 publications · 210 citations

    6

    h-index

1–5