Canopy Loss Detection

Canopy Loss Detection is a remote sensing methodology that identifies temporal reductions in vegetative canopy cover by analyzing spectral, spatial, and temporal anomalies in satellite imagery to distinguish disturbed forest areas from stable or naturally fluctuating conditions.

36

Publications

1.8K

Citations

156

Authors

95

Institutions

Publications per year

2017–2026

15

Authors

156

  1. AA
    Allard Anna

    Swedish University of Agricultural Sciences, Sweden

    2 publications · 38 citations

    2

    h-index

  2. DA
    Donat Agosti

    American Museum of Natural History, United States

    1 publications · 113 citations

    1

    h-index

  3. GJ
    Guillaume Jubelin

    Centre de Coopération Internationale en Recherche Agronomique pour le Développement, France

    1 publications · 17 citations

    1

    h-index

  4. LK
    Lammert Kooistra

    Wageningen University & Research, Netherlands

    1 publications · 122 citations

    1

    h-index

  5. AA
    Ayansina Ayanlade

    Obafemi Awolowo University, Nigeria

    1 publications · 28 citations

    1

    h-index

1–5 of 156

Venues

  1. Remote Sensing

    4 publications · 243 citations

    4

    h-index

  2. Remote Sensing of Environment

    2 publications · 493 citations

    2

    h-index

  3. Forest Ecology and Management

    2 publications · 84 citations

    2

    h-index

  4. 2

    h-index

  5. International Journal of Remote Sensing

    2 publications · 54 citations

    2

    h-index

1–5