@Article{RuizGaBrSiBoJa:2022:CoUsPh,
               author = "Ruiz, Isadora Haddad and Galv{\~a}o, L{\^e}nio Soares and 
                         Breunig, F{\'a}bio Marcelo and Silva, Ricardo Dalagnol and 
                         Bourscheidt, Vandoir and Jacon, Aline Daniele",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Santa Maria (UFSM)} and {} and {Universidade Federal de S˜ao 
                         Carlos (UFSCar)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "On the combined use of phenological metrics derived from different 
                         PlanetScope vegetation indices for classifying savannas in 
                         Brazil",
              journal = "Remote Sensing Applications: Society and Environment",
                 year = "2022",
               volume = "26",
                pages = "e100764",
             keywords = "Land Surface Phenology, Dry season, Ensemble metrics, Random 
                         Forest, Savannas, EVI, NDVI.",
             abstract = "Mapping of savannas in Brazil is challenging since there is no 
                         consensus on the best remote sensing strategy to deal with the 
                         spatial variability of some physiognomies and the spectral 
                         similarity of others. In this study, we evaluated the performance 
                         of 12 land surface phenology (LSP) metrics calculated from 70 
                         cloud-free PlanetScope (PS) satellite images and three vegetation 
                         indices (VIs) for Random Forest (RF) classification of eight 
                         savanna physiognomies. The 12 LSP metrics were: the start (SOS), 
                         end (EOS), length (LOS), and mean (MGS) of greening season; the 
                         mean spring (MSP) and mean autumn (MAU); the VI peak (PEAK) and 
                         trough (TRG); the positions of the peak (POP) and trough (POT); 
                         and the rates of spring green-up (RSP) and autumn senescence 
                         (RAU). These metrics were calculated from the Green-Red Normalized 
                         Difference (GRND), Enhanced vegetation Index (EVI), and Normalized 
                         Difference Vegetation Index (NDVI). At the protected Ecological 
                         Station of ´Aguas Emendadas (ESAE) in central Brazil, we compared 
                         the LSP classification in the 20172018 seasonal cycle against the 
                         VI classification in the 2017 dry season using an existent 
                         reference vegetation map for accuracy assessment. Furthermore, we 
                         analyzed the performance of the individual and combined sets of 
                         VIs and their derived LSP metrics for RF classification of the 
                         savanna physiognomies. The results showed that LSP added gains of 
                         19.3% (EVI), 13.1% (NDVI), and 5.4% (GRND) to dry-season VI 
                         classification. The overall accuracies of the individual and 
                         combined sets of VIs and their retrieved LSP metrics generated 
                         gains of 22.8% and 28.1% in relation to the dryseason EVI. In the 
                         classification combining LSP metrics, the most important ranked 
                         predictors originated from the NDVI and EVI (e.g., TRG, PEAK, MSP, 
                         MGS, and RSP). Our findings highlight the importance of the 
                         combined use of high spatial and temporal resolution data of the 
                         Planets satellite constellation for the classification of 
                         Brazilian savannas leveraging the information retrieved from 
                         vegetation phenology. However, when dense time series of a given 
                         sensor are not available for retrieving the phenological metrics, 
                         an alternative is to use combinedly different VIs calculated in 
                         the dry season, when the frequency of cloud cover is reduced over 
                         Brazilian savanna areas.",
                  doi = "10.1016/j.rsase.2022.100764",
                  url = "http://dx.doi.org/10.1016/j.rsase.2022.100764",
                 issn = "2352-9385",
             language = "en",
           targetfile = "[2022]HADDAD.et.al..pdf",
        urlaccessdate = "02 maio 2024"
}