Remote sensing of grasslands in the South American Pampas (scientometrics analysis)

Najila Souza da Rocha, Bijeesh Veetil, Raymond Ward, Juliani Costi, Silvia Beatriz Alves Rolim

Research output: Contribution to journalArticlepeer-review


Remotely sensed data contains a range of information (spectral, spatial, radiometric, and temporal) useful in mapping and monitoring grassland extent, minimizing the cost of field data collection and laboratory analysis, thereby improving management and conservation of these ecosystems. The South American pampa biome is one of the most productive ecosystems in the world consisting of an evergreen grassland environment in the southern areas of the continent (Brazil, Uruguay, and Argentina). In this review paper, a scientometrics analysis of the applications of grassland remote sensing in the South American pampa biome was performed with Web of Science Collection data using the relationship between authors/co-authors, institutions, countries, and keywords. Despite a large number of published works on the application of remote sensing in grassland ecosystems, comparatively, few have been conducted in the South American pampa biome. Of 166 published articles on remote sensing of grassland systems, the first one published on the pampas was in 1995, while research on this topic has been going on since 1985 in the rest of the world. Recently, some Journals have demonstrated interest in remote sensing of the pampa region: (a)IEEE Journal of Selected Topics in Applied Earth, (b) ISPRS Journal of Photogrammetry and Remote Sensing, and (c) Science of The Total Environment. A number of remote sensing methods applied elsewhere have yet to be applied in the South American Pampas, including LiDAR applications. A wide range of grassland parameters that can be estimated using remote sensing includes ecosystem health, alien species invasion, and biomass.

Original languageEnglish
Pages (from-to)2723-2734
Number of pages12
JournalLand Degradation & Development
Issue number10
Publication statusPublished - 20 Feb 2023


  • LiDAR
  • biome, ecosystems
  • vegetation mapping


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