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

Autor(es)

Chaves, Michel E. D. ; Soares, Livia G. D. ; Barros, Gustavo H. V. ; Pessoa, Ana Leticia F. ; Elias, Ronaldo O. ; Golzio, Ana Claudia ; Conceicao, Katyanne V. ; Morais, Flavio J. O.

• Tipo de documento: Artigo Científico
• Fonte: AGRIENGINEERING; v. 7, n. 1, p. 15-pg., 2025-01-01.

Resumo

The 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. (AU)

• Processo FAPESP: 21/07382-2 - Uso de séries temporais densas Sentinel-2/MSI e algoritmos de aprendizado de máquinas para melhorar o monitoramento agrícola no bioma Cerrado
• Beneficiário: Michel Eustáquio Dantas Chaves
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 23/09903-5 - DESENVOLVIMENTO DE SISTEMA EMBARCADO IoT APLICADO AO MONITORAMENTO DE PLANTAÇÃO DE SORGO
• Beneficiário: Ronaldo de Oliveira Elias
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 24/08083-7 - Identificação de lavouras de soja e milho no Cerrado brasileiro via imagens de satélite
• Beneficiário: Livia Gabriele Dias Soares
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica