Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes

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Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorState Secretariat for the Environment and Sustainability of Pará (SEMAS)-
Autor(es): dc.creatorChaves, Michel E. D.-
Autor(es): dc.creatorSoares, Lívia G. D.-
Autor(es): dc.creatorBarros, Gustavo H. V.-
Autor(es): dc.creatorPessoa, Ana Letícia F.-
Autor(es): dc.creatorElias, Ronaldo O.-
Autor(es): dc.creatorGolzio, Ana Claudia-
Autor(es): dc.creatorConceição, Katyanne V.-
Autor(es): dc.creatorMorais, Flávio J. O.-
Data de aceite: dc.date.accessioned2025-08-21T16:19:04Z-
Data de disponibilização: dc.date.available2025-08-21T16:19:04Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/agriengineering7010019-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/296952-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/296952-
Descrição: dc.descriptionThe conflict between environmental conservation and agricultural production highlights the need for precise land use and land cover (LULC) mapping to support agro-environmental-related policies. Satellite image time series from the Moderate Resolution Image Spectroradiometer (MODIS) sensor are essential for current LULC mapping efforts. However, most approaches focus on pixel data, and studies exploring object-based spatiotemporal heterogeneity and correlation features in its time series are limited. The objective of this study is to mix the data cube architecture (analysis-ready data—ARD) and the geo-object-oriented time series segmentation via Geographic Object-Based Image Analysis (GEOBIA) to assess its performance in identifying natural vegetation and double-cropping practices over a crop season. The study area was the state of Mato Grosso, Brazil. Results indicate that, by combining GEOBIA and time series analysis (materialized by the multiresolution segmentation algorithm to derive spatiotemporal geo-objects of the MODIS data cube), representative training data collected after a quality control process, and the Support Vector Machine to classify the ARD, the overall accuracy was 0.95 and all users’ and producers’ accuracies were higher than 0.88. By considering the heterogeneity of Mato Grosso’s landscape, the results indicate the potential of the approach to provide accurate mapping.-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences and Engineering-
Descrição: dc.descriptionState Secretariat for the Environment and Sustainability of Pará (SEMAS)-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences and Engineering-
Idioma: dc.languageen-
Relação: dc.relationAgriEngineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectcrop monitoring-
Palavras-chave: dc.subjectEarth observation data cubes-
Palavras-chave: dc.subjectGEOBIA-
Palavras-chave: dc.subjectsatellite image time series-
Palavras-chave: dc.subjectspectral indices-
Título: dc.titleMixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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