Remote Sensing-based Density Estimation

Remote Sensing-Based Density Estimation is a methodological framework within stand density estimation that utilizes remotely sensed data, such as aerial or satellite imagery, to quantitatively infer vegetation density through spatial, spectral, and structural analysis of canopy cover and plant biomass distribution.

68

Publications

2.8K

Citations

198

Authors

98

Institutions

Publications per year

2017–2026

27

Authors

198

  1. MF
    Michael F. Jasinski

    Goddard Space Flight Center, United States

    2 publications · 126 citations

    2

    h-index

  2. TW
    Thomas W. Gillespie

    University of California, Los Angeles, United States

    2 publications · 52 citations

    2

    h-index

  3. SS
    Sassan Saatchi

    Jet Propulsion Laboratory, United States

    2 publications · 52 citations

    2

    h-index

  4. MK
    Michael K. Early

    University of California, Los Angeles, United States

    2 publications · 52 citations

    2

    h-index

  5. SS
    Shaban Shataee

    Gorgan University of Agricultural Sciences and Natural Resources, Iran

    2 publications · 88 citations

    2

    h-index

1–5 of 198

Venues

  1. International Journal of Remote Sensing

    8 publications · 361 citations

    8

    h-index

  2. Remote Sensing

    5 publications · 184 citations

    6

    h-index

  3. 3

    h-index

  4. Remote Sensing of Environment

    3 publications · 206 citations

    3

    h-index

  5. Agricultural and Forest Meteorology

    2 publications · 131 citations

    2

    h-index

1–5