@Article{ReisEscDutSanVog:2018:CoReSe,
               author = "Reis, Mariane Souza and Escada, Maria Isabel Sobral and Dutra, 
                         Luciano Vieira and Sant'Anna, Sidnei Jo{\~a}o Siqueira and Vogt, 
                         Nathan David",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade do Vale do Para{\'{\i}}ba}",
                title = "Towards a Reproducible LULC Hierarchical Class Legend for Use in 
                         the Southwest of Par{\'a} State, Brazil: A Comparison with Remote 
                         Sensing Data-Driven Hierarchies",
              journal = "Land",
                 year = "2018",
               volume = "7",
               number = "2",
                pages = "1--29",
             keywords = "land use and land cover(LULC), Brazilian Amazon, remote sensing, 
                         class definition, field data collection, land cover meta 
                         language(LCML), classification system.",
             abstract = "Land Use and Land Cover (LULC) classes defined by subjective 
                         criteria can diminish the significance of a study, hindering the 
                         reproducibility and the comparison of results with other studies. 
                         Having a standard legend for a given study area and objective 
                         could benefit a group of researchers focused on long-term or 
                         multidisciplinary studies in a given area, in the sense that they 
                         would be able to maintain class definition among different works, 
                         done by different teams. To allow for reproducibility, it is 
                         important that the classes in this legend are described using 
                         quantifiable elements of land cover, which can be measured on the 
                         ground, as is recommended by Land Cover Meta Language (LCML). The 
                         present study aims to propose LCML formalized hierarchical legends 
                         for LULC classes, focusing on the southwest of Par{\'a} state, 
                         within the Brazilian Amazon. In order to illustrate the potential 
                         of these legends, a secondary objective of the current study is to 
                         analyze classification results using legends derived from a 
                         particular Remote Sensing dataset and compare these results with 
                         the classification obtained using the LCML hierarchical legend 
                         proposed. To perform this analysis, firstly, we proposed a 
                         conceptual class model based on existing classification systems 
                         for the upland Brazilian Amazon Biome. From this model, 16 LULC 
                         classes were described in LCML, using quantifiable and easily 
                         recognizable physiognomic characteristics of land cover classes 
                         measured on the lower Tapaj{\'o}s river, in Par{\'a} state. 
                         These classes were grouped into legends with different levels of 
                         detail (number of classes), based on our model or on the image and 
                         clustering algorithms. All legends were used in supervised 
                         classification of a Landsat5/TM image. Results indicate that it is 
                         necessary to incorporate multi-temporal knowledge for class 
                         definition as well as the proposed thresholds (height and cover 
                         proportion of soil, litter, herbaceous vegetation, shrubs, and 
                         trees) in order to properly describe classes. However, the 
                         thresholds are useful to delimit classes that happen in a 
                         successive way. Classification results revealed that classes 
                         formed by the same elements of land cover with similar thresholds 
                         present high confusion. Additionally, classifications obtained 
                         using legends based on the class separability in a given Remote 
                         Sensing image tend to be more accurate but not always useful 
                         because they can hide or mix important classes. It was observed 
                         that the more generalized the legend (those with few details and 
                         number of classes), the more accurate the classifications results 
                         are for all types of legends.",
                 issn = "2073-445X",
                label = "lattes: 3997386421385499 4 ReisDutSanVog:2018:CoReSe",
             language = "en",
           targetfile = "reis_towards.pdf",
        urlaccessdate = "27 abr. 2024"
}