Mapping 33 years of sugarcane evolution in São Paulo state, Brazil, using landsat imagery and generalized space-time classifiers

Luciano Ana Cláudia dos Santos, Campagnuci Bruna Cristina Gama, Le Maire Guerric. 2022. Mapping 33 years of sugarcane evolution in São Paulo state, Brazil, using landsat imagery and generalized space-time classifiers. Remote Sensing Applications: Society and Environment, 26:100749, 12 p.

Url - jeu de données - Dataverse Cirad : https://doi.org/10.18167/DVN1/DQXFZG

Résumé : Remote sensing techniques can help estimate large sugarcane areas in order to support sustainable planning and management for the sugarcane industry, as well as in the context of environmental monitoring. In this study, four generalized space-time classifiers of high-resolution satellite images were tested in São Paulo State (SP) in order to map out sugarcane areas over the course of 33 years. These classification models were created based on the random forest (RF) algorithm, using Landsat historical imagery from 1986 to 2019 as input data. Models were calibrated with reference data under the sugarcane and "other uses" classes over 10 sites across the state of São Paulo and applied to the entire state area. The four models differed in their use of input data from the different Landsat sensors. The Dice Coefficient (DC) accuracies across sites ranged between 0.60 and 0.90. The best model employed data from all sensors and was calibrated with data from 6 consecutive years. When this model was applied on independent years, the DC values averaged at 0.82. The sugarcane areas estimated from satellite imagery demonstrated a notable correlation with areas estimated from reference Canasat maps (R2 = 0.91), data from the Brazilian Institute of Geography and Statistics (R2 = 0.92), and data from the Brazilian National Supply Company (R2 = 0.73). The general trend was a large increase in sugarcane areas starting in the early 90s up until 2015, and a trend toward stabilization, and even small decreases, after 2015. This study showed that a single classification model trained on 6 years of data allowed for the accurate monitoring of sugarcane areas over 33 years, without the tedious and time- consuming work of re-calibrating the classifier for previous or new years. Year-to-year accuracy, however, largely depended on the availability and quality of Landsat imagery. The results were applicable to the monitoring of sugarcane areas over large areas and numerous years, which could assist the sugarcane energy sector.

Mots-clés Agrovoc : cartographie de l'occupation du sol, cartographie de l'utilisation des terres, Landsat, télédétection, imagerie par satellite, canne à sucre

Mots-clés géographiques Agrovoc : Brésil

Classification Agris : E11 - Économie et politique foncières
U30 - Méthodes de recherche
P01 - Conservation de la nature et ressources foncières

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Agences de financement hors UE : United Nations Development Programme, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Fundação de Amparo à Pesquisa do Estado de São Paulo

Auteurs et affiliations

• Luciano Ana Cláudia dos Santos, UNICAMP (BRA) - auteur correspondant
• Campagnuci Bruna Cristina Gama, UNICAMP (BRA)
• Le Maire Guerric, CIRAD-PERSYST-UMR Eco&Sols (FRA) ORCID: 0000-0002-5227-958X

Source : Cirad-Agritrop (https://agritrop.cirad.fr/601414/)

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