@Article{AdornoKörtAmar:2023:CoTiDa,
               author = "Adorno, Bruno Vargas and K{\"o}rting, Thales Sehn and Amaral, 
                         Silvana",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Contribution of time-series data cubes to classify urban 
                         vegetation types by remote sensing",
              journal = "Urban Forestry and Urban Greening",
                 year = "2023",
               volume = "79",
                pages = "e127817",
                month = "Jan.",
             keywords = "CBERS-4A WPM, Multisource image analysis, Object-based 
                         classification, Per-pixel classification, Sentinel-2 MSI.",
             abstract = "Mapping urban vegetation types is important for urban planning and 
                         assessing environmental justice. Nowadays, despite data cubes 
                         projects are providing Analysis Ready Data to facilitate 
                         time-series analysis, we did not found studies employing these 
                         data for improving urban vegetation mapping. By relying solely on 
                         open data and software, this work proposes and evaluates the 
                         integration of time-series data cubes in a hybrid image 
                         classification method to map the intra-urban space, 
                         differentiating Tree cover and Herb-shrub. The urban area of 
                         Goi{\^a}nia, Goi{\'a}s, Brazil, is the study area. The hybrid 
                         method combined object-based classification of a pan-sharpened 
                         CBERS-4A WPM image (spatial resolution of 2 m) with the 
                         pixel-based classification of Sentinel-2 MSI time-series data 
                         cubes (10 m). Both approaches used the Random Forest algorithm. 
                         Objects from the CBERS-4A segmentation composed the spatial unit 
                         of analysis and the class assignment depended on the Sentinel-2 
                         time-series urban land cover probabilities. Based on both Maps 
                         probabilities, Shannon entropy was calculated to attribute the 
                         final urban land cover to the objects. Urban land cover 
                         probabilities presented similar spatial distribution patterns for 
                         both classification approaches. Regarding the thematic maps, the 
                         Herb-shrub cover area was 35% higher in Sentinel-2 time-series 
                         classification than in GEOBIA classification, but Tree cover was 
                         21% lower. In general, 75% of the study area was equally 
                         classified by the initial approaches. However, for 9% of the 
                         remaining area, the hybrid classification improved vegetation 
                         classes accuracies by 35%, contributing to the vegetation covers 
                         identification. Thus, this study contributes to methodological 
                         procedures for urban land cover study and demonstrates that hybrid 
                         maps based on open data are effective to reduce classification 
                         mistakes, allowing more accurate monitoring, planning, and 
                         designing of different urban vegetation types. Future research 
                         efforts should focus on scale compatibility between data of 
                         different spatial resolutions and expand the use of data cubes to 
                         integrate time-series information into the GEOBIA 
                         classification.",
                  doi = "10.1016/j.ufug.2022.127817",
                  url = "http://dx.doi.org/10.1016/j.ufug.2022.127817",
                 issn = "1618-8667",
             language = "en",
           targetfile = "1-s2.0-S1618866722003600-main.pdf",
        urlaccessdate = "12 maio 2024"
}