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A New Climatology of Vegetation and Land Cover Information for South America

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Listed:
  • Laurizio Emanuel Ribeiro Alves

    (Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil)

  • Luis Gustavo Gonçalves de Gonçalves

    (Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil
    Fundazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC), 73100 Lecce, Italy)

  • Álvaro Vasconcellos Araújo de Ávila

    (Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil)

  • Giovana Deponte Galetti

    (Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil)

  • Bianca Buss Maske

    (Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), Instituto Nacional de Pesquisas Espaciais (INPE), Cachoeira Paulista 12630-000, Brazil)

  • Giuliano Carlos do Nascimento

    (Centro de Monitoramento de Alerta e Alarme da Defesa Civil (CEMADEC), Defesa Civil de Salvador (CODESAL), Salvador 40301-110, Brazil)

  • Washington Luiz Félix Correia Filho

    (Programa de Pós-Graduação em Ambientometria, Universidade Federal do Rio Grande (FURG), Rio Grande 96203-900, Brazil)

Abstract

Accurate information on vegetation and land cover is crucial for numerical forecasting models in South America. This data aids in generating more realistic forecasts, serving as a tool for decision-making to reduce environmental impacts. Regular updates are necessary to ensure the data remains representative of local conditions. In this study, we assessed the suitability of ‘Catchment Land Surface Models-Fortuna 2.5’ (CLSM), Noah, and Weather Research and Forecasting (WRF) for the region. The evaluation revealed significant changes in the distribution of land cover classes. Consequently, it is crucial to adjust this parameter during model initialization. The new land cover classifications demonstrated an overall accuracy greater than 80%, providing an improved alternative. Concerning vegetation information, outdated climatic series for Leaf Area Index (LAI) and Greenness Vegetation Fraction (GVF) were observed, with notable differences between series, especially for LAI. While some land covers exhibited good performance for GVF, the Forest class showed limitations. In conclusion, updating this information in models across South America is essential to minimize errors and enhance forecast accuracy.

Suggested Citation

  • Laurizio Emanuel Ribeiro Alves & Luis Gustavo Gonçalves de Gonçalves & Álvaro Vasconcellos Araújo de Ávila & Giovana Deponte Galetti & Bianca Buss Maske & Giuliano Carlos do Nascimento & Washington Lu, 2024. "A New Climatology of Vegetation and Land Cover Information for South America," Sustainability, MDPI, vol. 16(7), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2606-:d:1361619
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    References listed on IDEAS

    as
    1. Giovana Mira Espindola & Elayne Silva Figueredo & Péricles Picanço Júnior & Antonio Aderson Reis Filho, 2021. "Cropland expansion as a driver of land-use change: the case of Cerrado-Caatinga transition zone in Brazil," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 17146-17160, November.
    2. Liang Chen & Zhuguo Ma & Tianbao Zhao, 2017. "Modeling and analysis of the potential impacts on regional climate due to vegetation degradation over arid and semi-arid regions of China," Climatic Change, Springer, vol. 144(3), pages 461-473, October.
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