@Article{SouzaGalvKörtAlme:2021:DaApDe,
               author = "Souza, Alana Almeida de and Galv{\~a}o, L{\^e}nio Soares and 
                         K{\"o}rting, Thales Sehn and Almeida, Cl{\'a}udio Aparecido de",
          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)}",
                title = "On a Data-Driven Approach for Detecting Disturbance in the 
                         Brazilian Savannas Using Time Series of Vegetation Indices",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "24",
                pages = "4959",
             keywords = "Cerrado, Disturbance, fire, Clearing, Vegetation Indices, CCDC.",
             abstract = "Remote sensing of disturbance in the savannas from Brazil is 
                         challenging, especially due to confounding effects of the 
                         vegetation phenology and natural soil exposure on the detection of 
                         clearing and fire events. In this study, we investigated the 
                         detection of disturbance over this global hotspot of biodiversity 
                         using seven vegetation indices (VIs) calculated from the Landsat 
                         time series (20172019) and the Continuous Change Detection and 
                         Classification (CCDC) algorithm. The selected VIs represented 
                         distinct biophysical characteristics of the savannas. We evaluated 
                         the effects of disturbance on these VIs and assessed the accuracy 
                         of CCDC-detection in 2019, considering individual VIs, ensemble 
                         VIs, and the type of disturbance (savanna clearing and fire). 
                         Finally, we analyzed the possible existence of seasonal patterns 
                         of disturbance in a study area located at the new agricultural 
                         frontier of the Cerrado biome. The results showed that the overall 
                         accuracy of CCDC detection of total disturbance ranged from 51.2% 
                         for the Green-Red Normalized Difference (GRND) to 65.9% for the 
                         Normalized Burn Ratio (NBR2). It increased to 71.2% for ensemble 
                         VIs, whose multivariate approach reduced the omission errors in 
                         the analysis when compared to the use of single VIs. For detecting 
                         events of savanna clearing and fire, the most important VIs used 
                         near-infrared and shortwave infrared reflectance bands on their 
                         formulations (NBR2, NBR, and Moisture Stress IndexMSI). The CCDC 
                         accuracy was generally higher for detecting clearing than for 
                         mapping burned areas. In contrast, the recorded date of 
                         disturbance occurrence was less precise for detecting clearing 
                         than for recording events caused by fire, especially due to the 
                         existence of some gradual processes of vegetation degradation 
                         until complete clearing. Our findings showed also the existence of 
                         a seasonal pattern of disturbance occurrence. Savanna clearing 
                         predominated in the transition from the rainy to the dry season 
                         (April to July) to open new areas for agriculture. It preceded 
                         most events of fire disturbance between August and October that 
                         occurred near the consolidated areas of agriculture and extended 
                         into the native vegetation areas. Results reinforce the importance 
                         of data-driven approaches for generating early warning alerts of 
                         disturbance in the Cerrado to be further checked in the field.",
                  doi = "10.3390/rs13244959",
                  url = "http://dx.doi.org/10.3390/rs13244959",
                 issn = "2072-4292",
                label = "lattes: 5507769922001047 2 SouzaGalvKortAlme:2021:DaApDe",
             language = "en",
           targetfile = "alana_data.pdf",
        urlaccessdate = "12 maio 2024"
}