Igor da Silva Narvaes

DETER-B: The New Amazon Near Real-Time Deforestation Detection System

The Brazilian Legal Amazon (BLA), the largest global rainforest on earth, contains nearly 30% of the rainforest on earth. Given the regional complexity and dynamics, there are large government investments focused on controlling and preventing deforestation. The National Institute for Space Research (INPE) is currently developing five complementary BLA monitoring systems, among which the near real-time deforestation detection system (DETER) excels. DETER employs MODIS 250 m imagery and almost daily revisit, enabling an early warning system to support surveillance and control of deforestation. The aim of this paper is to present the methodology and results of the DETER based on AWIFS data, called DETER-B. Supported by 56 m images, the new system is effective in detecting deforestation smaller than 25 ha, concentrating 80% of its total detections and 45% of the total mapped area in this range. It also presents higher detection capability in identifying areas between 25 and 100 ha. The area estimation per municipality is statistically equal to those of the official deforestation data (PRODES) and allows the identification of degradation and logging patterns not observed with the traditional DETER system.

Validation of TerraClass mapping for the Municipality of Paragominas state of Pará

This work aims to evaluate the TerraClass mapping for the year 2014, in the municipality of Paragominas, State of Para. The validation was made by comparing the mapping with the observations found in the field. Images of the Satetile Landsat-8, OLI sensor of the year 2014, path/row 222/062, 222/063, 223/062 and 223/063 were used to aid in the field. Using this data it was possible to analyze the main representative classes in the area, including agriculture, urban area, forest, clean pasture, dirty pasture, reforestation, regeneration with pasture and secondary vegetation. The secondary vegetation presented 2,198.16 km², clean pasture with 3,332.29 km², agriculture with 896.75 km² and the forest occupying 54.21% of the total area of Paragominas. The overall concordance index was 86%, corroborating the reliability of the mapping performed. The average error was 6% and the total value of discordance was of 14%. Concerning the secondary vegetation, pasture, agriculture, urban area and forest classes, they presented concordance higher to 50%, while regeneration with pasture and reforestation presented greater intensity of omission with 40,57% and 76,31% respectively. Inclusion errors were less than 40% for the secondary vegetation, pasture regeneration, clean pasture and dirty pasture classes. The field work was essential to validate and analyze the accuracy of the 2014 TerraClass Project for the studied region, which becomes important for the understanding of the dynamics of land use.

Distinção de espécies de eucaliptode diferentes idades por meio de imagens TM/Landsat 5

The objective of this work was to evaluate the use of the TM/Landsat 5 sensor imagens in the differentiation of commercial plantations of Eucalyptus urograndis and Eucalyptus dunnii of different ages. Plots were marked for identification of the two Eucalyptus species, in two different periods (2009 and 2011): for 3- and 5-year-old E. dunnii, and for 2.2- and 4.2-year-old E. urograndis. Evaluations were done for six bands of the TM/Landsat 5 sensor (B1, B2, B3, B4, B5, and B7), and for six vegetation indices - simple ratio (SR); and normalized difference vegetation index (NDVI); soil-adjusted vegetation index (Savi)-0.25; Savi-0.5; green NDVI (GNDVI); and the moisture-vegetation index (MVI). Image digital processing consisted of geometric, radiometric, and atmospheric corrections. The plantations of E. dunnii and E. urograndis were distinguished by five bands of Landsat (B2, B3, B4, B5, and B7) and three vegetation indices (Savi-0.5, Savi-0.25, and GNDVI), in 2009, and by four bands of Landsat (B2, B4, B5, and B7) and six vegetation indices (NDVI, SR, Savi-0.5, Savi-0.25, MVI, and GNDVI) in 2011. The spectral data extracted from TM/Landsat 5 images are effective, both to distinguish the species of eucalyptus as well as the same species in even-aged stand.