Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes
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 essen...
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MDPI AG
2025-01-01
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| author | Michel E. D. Chaves Lívia G. D. Soares Gustavo H. V. Barros Ana Letícia F. Pessoa Ronaldo O. Elias Ana Claudia Golzio Katyanne V. Conceição Flávio J. O. Morais |
| author_facet | Michel E. D. Chaves Lívia G. D. Soares Gustavo H. V. Barros Ana Letícia F. Pessoa Ronaldo O. Elias Ana Claudia Golzio Katyanne V. Conceição Flávio J. O. Morais |
| author_sort | Michel E. D. Chaves |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-357daeaf17b0479286e6a82d8c68e446 |
| institution | Kabale University |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-357daeaf17b0479286e6a82d8c68e4462025-01-24T13:16:16ZengMDPI AGAgriEngineering2624-74022025-01-01711910.3390/agriengineering7010019Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous LandscapesMichel E. D. Chaves0Lívia G. D. Soares1Gustavo H. V. Barros2Ana Letícia F. Pessoa3Ronaldo O. Elias4Ana Claudia Golzio5Katyanne V. Conceição6Flávio J. O. Morais7São Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, BrazilSão Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, BrazilSão Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, BrazilSão Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, BrazilSão Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, BrazilSão Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, BrazilState Secretariat for the Environment and Sustainability of Pará (SEMAS), Belém 66040-170, BrazilSão Paulo State University (UNESP), School of Sciences and Engineering, Tupã 17602-496, BrazilThe 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.https://www.mdpi.com/2624-7402/7/1/19satellite image time seriesEarth observation data cubesGEOBIAspectral indicescrop monitoring |
| spellingShingle | Michel E. D. Chaves Lívia G. D. Soares Gustavo H. V. Barros Ana Letícia F. Pessoa Ronaldo O. Elias Ana Claudia Golzio Katyanne V. Conceição Flávio J. O. Morais Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes AgriEngineering satellite image time series Earth observation data cubes GEOBIA spectral indices crop monitoring |
| title | Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes |
| title_full | Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes |
| title_fullStr | Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes |
| title_full_unstemmed | Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes |
| title_short | Mixing Data Cube Architecture and Geo-Object-Oriented Time Series Segmentation for Mapping Heterogeneous Landscapes |
| title_sort | mixing data cube architecture and geo object oriented time series segmentation for mapping heterogeneous landscapes |
| topic | satellite image time series Earth observation data cubes GEOBIA spectral indices crop monitoring |
| url | https://www.mdpi.com/2624-7402/7/1/19 |
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