Upturn in secondary forest clearing buffers primary forest loss in the Brazilian Amazon

Brazil contains two-thirds of remaining Amazonian rainforests and is responsible for the most Amazon forest loss. Primary forest loss in the Brazilian Amazon has declined considerably since 2004 but secondary forest loss has never been quantified. We use a recently developed high-resolution land use/land cover dataset to track secondary forests in the Brazilian Amazon over 14 yr, providing the first estimates of secondary forest loss for the region. We find that secondary forest loss increased by (187 ± 48)% from 2008 to 2014. Moreover, the proportion of total forest loss accounted for by secondary forests rose from (37 ± 3)% in 2000 to (72 ± 5)% in 2014. The recent acceleration in secondary forests loss occurred across the entire region and was not driven simply by increasing secondary forest area but probably a conscious preferential shift towards clearance of a little-protected forest ecosystem (secondary forests). Our results suggest that secondary forests loss has eased deforestation pressure on primary forests. However, this has been at the expense of a lost carbon sequestration opportunity of 2.59–2.66 Pg C over our study period

Use of NDVI/AVHRR time-series profiles for soybean crop monitoring in Brazil

In Brazil there is a need for less subjective, more efficient and less expensive methodologies for crop yield forecast. Owing to the continental dimensions of the country, orbital images have been used to estimate the productive potential of crops. In this study, NDVI (Normalized Difference Vegetation Index) time-series, derived from AVHRR/NOAA (Advanced Very High Resolution Radiometer/National Oceanic and Atmospheric Administration) imagery were used for the soybean crop monitoring in a large production region in Brazil in the 2002/2003 and 2003/2004 cropping seasons. NDVI temporal profiles describing the biomass condition of crops throughout the phenological stages were generated in 18 municipalities. Quantitative parameters were measured from the temporal profiles, based on the full time or partial phenological cycle. Linear regressions between the quantitative parameters and the municipal average yields in both seasons have shown that the most significant correlations occurred when the full time period was considered. When considering periods prior to harvest, the correlations showed a tendency to decline. The NDVI monitoring during these two cropping seasons, which presented different weather conditions, could explain a major part of the soybean yield variability at the municipal level. Results showed the potential of the NDVI time-series analysis in generating parameters to be employed by agrometeorological-spectral models for soybean yield estimations. The automatic system for temporal profiles generation developed in this study sped up the analysis and can be used for further studies at a regional scale.321337113727UNICAMP (Universidade Estadual de Campinas

An automatic system for AVHRR land surface product generation

Making products from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites can be time consuming and an automated technique for image processing is required to generate long time series of AVHRR imagery. This paper aims to describe the development of a system for fully-automated AVHRR image processing, including radiometric calibration, precise geo-registration and generating land-surface products, such as vegetation indices, maximum value composites and cloud masks. Tests for crop monitoring purposes were carried out using High Resolution Picture Transmission (HRPT) images between October 2003 and April 2004. The region used to evaluate the system was the State of Parana, one of the primary soybean producers in Brazil. Results have shown that for severely cloud-filtered images, the system was effective in generating geometrically precise image products, with geolocation errors less than a pixel. The developed system can be operated with no human intervention and can be used as an important tool for NOAA-AVHRR image users especially those who need to use long time series.27183925394