@InProceedings{UgarteNiedSantMald:2007:GuBo,
               author = "Ugarte, H. Ferrufino and Niedzwiecki, T. Zawila and Santos, 
                         Jo{\~a}o Roberto dos and Maldonado, F. D.",
          affiliation = "{University of Applied Sciences Fh – Eberswalde – Forestry 
                         Faculty} and {University of Applied Sciences Fh – Eberswalde – 
                         Forestry Faculty} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas da Amaz{\^o}nia - 
                         INPA}",
                title = "Change Detection in the Amazon Rainforest with Radiometric 
                         Rotation Technique RCEN Multi-spectral Case Study: Guarayos – 
                         Bolivia",
                 year = "2007",
                pages = "4",
         organization = "IEEE Insternational Geoscience and Remote Sensing Symposium.",
             keywords = "change detection, degradation, CBERS-2, tropical forest, remote 
                         sensing, Guarayos.",
             abstract = "A working group of three institutions was set up to develop this 
                         study: University of Applied Sciences Eberswalde (Germany), 
                         National Institute for Space Research (INPE, Brazil) and National 
                         Institute for Amazon Research (INPA, Brazil). The main task is to 
                         apply in the Guarayos region (Bolivia), the multitemporal change 
                         detection algorithm RCEN multi-spectral. The study area is located 
                         in Guarayos-Bolivia, characterized by two main high forests 
                         landscapes, The Amazon Region and The Brazilian Paranaense Region; 
                         the approach of the change detection is taken under multivariate 
                         analysis (three spectral bands), with data coming from two kinds 
                         of sensor ETM+/Landsat-7 and CCD/CBERS-2. The image detection was 
                         transformed from continuous image (floating-point) to thematic, 
                         through slicing and labeling process. Hence it is possible to 
                         discriminate five thematic classes: two related to degradation, 
                         two referring to regeneration and one of no-change. The change 
                         detection map shows: in the timeframe studied 11% of all area 
                         under study presents deforestation patterns, on the other hand the 
                         regeneration class is not significant. In conclusion the 
                         methodology has good performance and it is evolving in landscapes 
                         with high humidity complications.",
  conference-location = "Barcelona, Espanha",
             language = "en",
           targetfile = "jo{\~a}o roberto2.pdf",
        urlaccessdate = "11 maio 2024"
}