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@Article{BreunigGaDaSaDeCh:2020:AsEfSp,
               author = "Breunig, F{\'a}bio Marcelo and Galv{\~a}o, L{\^e}nio Soares and 
                         Dalagnol da Silva, Ricardo and Santi, Ant{\^o}nio Luiz and Della 
                         Flora, Diandra Pinto and Chen, Shuisen",
          affiliation = "{Universidade Federal de Santa Maria (UFSM)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Federal de Santa 
                         Maria (UFSM)} and {Universidade Federal da Grande Dourados (UFGD)} 
                         and {Guangzhou Institute of Geography}",
                title = "Assessing the effect of spatial resolution on the delineation of 
                         management zones for smallholder farming in southern Brazil",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2020",
               volume = "19",
                pages = "e100325",
                month = "Aug.",
             keywords = "Cover crops, UAV, PlanetScope, OLI/Landsat-8, Agriculture.",
             abstract = "Remote sensing estimates of cover-crop aboveground biomass (AGB) 
                         have been used to delineate management zones for smallholder 
                         farming in southern Brazil. In this study, we investigated the 
                         spatial resolution influence on the AGB estimates of rye, 
                         calculated from regression relationships with the Normalized 
                         Difference Vegetation Index (NDVI), and on the subsequent 
                         delineation of management zones using the Management Zone Analyst 
                         (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) 
                         Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were 
                         compared with observations from the PlanetScope (PS) satellite 
                         constellation (3 m) and the Operational Land Imager 
                         (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green 
                         vegetation, soil, and shadow) was applied to surface reflectance 
                         data of these instruments for evaluating the cover-crop 
                         development at two dates in August 2017. Because of the 
                         differences in the technical specifications of the sensors, we 
                         resampled the UAV dataset into four levels of spatial resolution 
                         (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, 
                         we obtained confusion matrices for the original and resampled 
                         data. The results showed that the increasing amounts of rye AGB 
                         from the beginning to the end of August promoted significant 
                         changes in surface reflectance and in soil-green vegetation 
                         fractions calculated at variable spatial resolution. The 
                         performance of the regression models to estimate cover-crop AGB 
                         was approximately similar in the transition from the sub-metric 
                         (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV 
                         camera to the PS data. For all datasets, the MZA detected two 
                         management zones with zone 2 having higher cover-crop AGB than 
                         zone 1. When compared to the UAV management zone map (reference), 
                         the PS map had a moderate-to-substantial agreement, while the 
                         OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial 
                         agreements with the reference map were observed at simulated 1 m 
                         and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 
                         and overall accuracies of 86.40% and 81.40%, respectively. We 
                         conclude that the 3 m spatial resolution data of the PS comprise 
                         an alternative to delineate management zones for smallholder 
                         farming in southern Brazil when compared to the very-high spatial 
                         resolution observations of the UAV cameras.",
                  doi = "10.1016/j.rsase.2020.100325",
                  url = "http://dx.doi.org/10.1016/j.rsase.2020.100325",
                 issn = "2352-9385",
             language = "en",
           targetfile = "breunig_assessing.pdf",
        urlaccessdate = "18 abr. 2021"
}


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