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AgriEngineering
Volume 7
Issue 1
10.3390/agriengineering7010019
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Michel E. D. Chaves
1,*,
Lívia G. D. Soares
1,
Gustavo H. V. Barros
1,
Ana Letícia F. Pessoa
1,
Ronaldo O. Elias
1,
Ana Claudia Golzio
1,
Katyanne V. Conceição
2 and
Flávio J. O. Morais
1
1
São Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, Brazil
2
State Secretariat for the Environment and Sustainability of Pará (SEMAS), Belém 66040-170, Brazil
*
Author to whom correspondence should be addressed.
AgriEngineering 2025, 7(1), 19; https://doi.org/10.3390/agriengineering7010019
Submission received: 6 December 2024 / Revised: 13 January 2025 / Accepted: 14 January 2025 / Published: 17 January 2025
(This article belongs to the Special Issue Research Progress and Challenges of Agricultural Information Technology)
Versions Notes

Abstract

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.
Keywords: satellite image time series; Earth observation data cubes; GEOBIA; spectral indices; crop monitoring satellite image time series; Earth observation data cubes; GEOBIA; spectral indices; crop monitoring

Share and Cite

MDPI and ACS Style

Chaves, M.E.D.; Soares, L.G.D.; Barros, G.H.V.; Pessoa, A.L.F.; Elias, R.O.; Golzio, A.C.; Conceição, K.V.; Morais, F.J.O. Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes. AgriEngineering 2025, 7, 19. https://doi.org/10.3390/agriengineering7010019

AMA Style

Chaves MED, Soares LGD, Barros GHV, Pessoa ALF, Elias RO, Golzio AC, Conceição KV, Morais FJO. Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes. AgriEngineering. 2025; 7(1):19. https://doi.org/10.3390/agriengineering7010019

Chicago/Turabian Style

Chaves, Michel E. D., Lívia G. D. Soares, Gustavo H. V. Barros, Ana Letícia F. Pessoa, Ronaldo O. Elias, Ana Claudia Golzio, Katyanne V. Conceição, and Flávio J. O. Morais. 2025. "Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes" AgriEngineering 7, no. 1: 19. https://doi.org/10.3390/agriengineering7010019

APA Style

Chaves, M. E. D., Soares, L. G. D., Barros, G. H. V., Pessoa, A. L. F., Elias, R. O., Golzio, A. C., Conceição, K. V., & Morais, F. J. O. (2025). Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes. AgriEngineering, 7(1), 19. https://doi.org/10.3390/agriengineering7010019

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