The CHOVE-CHUVA Earth observation platform to monitor socio-environmental dynamics in Mato Grosso, Brazil

Remote sensing science is expected to produce spatio-temporal indicators to help societies to address major global challenges. In this regard, we have implemented the CHOVE-CHUVA web platform to monitor socio-environmental dynamics in the Brazilian Amazon state of Mato Grosso. Result of a long-term collaboration between research labs, local NGOs, and administrations, this Space for Climate Observatory initiative relies on two major pillars: (1) visualizing and computing spatio-temporal indices derived from Earth Observation data and (2) collecting citizen information as part of collaborative science. A major asset of the platform is to gather, visualize, and process data covering a wide range of themes such as land status, land use, climate, natural vegetation, agriculture, and hydrology. The collaborative information refers to land use types that are still unusual in Mato Grosso, i.e., forest restoration and low-carbon agricultural practices. The implementation of the platform was based on a French open source geospatial data infrastructure named PRODIGE. Prospects for enhancing the platform include integrating new thematic information, making better use of raw Earth Observation data, improving interactions with end-users to better capture their interpretation of socio-environmental dynamics, and improving the platform's efficiency to update data and process large study areas.

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Publication Details

Journal
Scientific Reports
Published
2026-01-28
DOI
https://doi.org/10.1038/s41598-026-36640-w
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

The CHOVE-CHUVA Earth observation platform to monitor socio-environmental dynamics in Mato Grosso, Brazil

Arnaud Bellec, Agnés Bégué, V. Dubreuil, Beatriz M. Funatsu et al.
Scientific Reports
Remote Sensing in Agriculture
article

The CHOVE-CHUVA Earth observation platform to monitor socio-environmental dynamics in Mato Grosso, Brazil

Arnaud Bellec, Agnés Bégué, V. Dubreuil, Beatriz M. Funatsu, Patrick Calvano Küchler, Julie Betbeder Betbeder, M. Simões, André Pereira Dias, Damien Arvor, Julien Denize, Rodrigo P. D. Ferraz, Léa Rouxel, Uelison M. Ribeiro, Carlos A. da Silva, Vinicius Silgueiro
article en

Abstract

Remote sensing science is expected to produce spatio-temporal indicators to help societies to address major global challenges. In this regard, we have implemented the CHOVE-CHUVA web platform to monitor socio-environmental dynamics in the Brazilian Amazon state of Mato Grosso. Result of a long-term collaboration between research labs, local NGOs, and administrations, this Space for Climate Observatory initiative relies on two major pillars: (1) visualizing and computing spatio-temporal indices derived from Earth Observation data and (2) collecting citizen information as part of collaborative science. A major asset of the platform is to gather, visualize, and process data covering a wide range of themes such as land status, land use, climate, natural vegetation, agriculture, and hydrology. The collaborative information refers to land use types that are still unusual in Mato Grosso, i.e., forest restoration and low-carbon agricultural practices. The implementation of the platform was based on a French open source geospatial data infrastructure named PRODIGE. Prospects for enhancing the platform include integrating new thematic information, making better use of raw Earth Observation data, improving interactions with end-users to better capture their interpretation of socio-environmental dynamics, and improving the platform's efficiency to update data and process large study areas.

Scientific ReportsVol. 16(1)
Centre National de la Recherche Scientifique (FR), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (FR), Université de Bretagne Occidentale (FR), Université de Montpellier (FR), Brazilian Agricultural Research Corporation (BR), AgroParisTech (FR), Université Rennes 2 (FR), Alstom (France) (FR), Universidade do Estado do Rio de Janeiro (BR), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Forests and Societies (FR), Secretaria do Meio Ambiente (BR), Instituto Florestal (BR), Institut Agro Montpelier (FR), Littoral, Environnement, Télédétection, Géomatique (FR), Université de Rennes (FR), Sinop University (TR)
Openalex Percentile: Top 78%
Remote Sensing in Agriculture
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