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The objective of this paper is to present a method for mapping burnt areas in Brazilian Amazonia using Terra MODIS data. The proposed approach is based on image segmentation of the shade fraction images derived from MODIS, using a non-supervised classification algorithm followed by an image editing procedure for minimizing misclassifications. Acre State, the focus of this study, is located in the western region of Brazilian Amazonia and undergoing tropical deforestation. The extended dry season in 2005 affected this region creating conditions for extensive forest fires in addition to fires associated with deforestation and land management. The high temporal resolution of MODIS provides information for studying the resulting burnt areas. Landsat 5 TM images and field observations were also used as ground data for supporting and validating the MODIS results. Multitemporal analysis with MODIS showed that about 6500 km2 of land surface were burnt in Acre State. Of this, 3700 km2 corresponded to the previously deforested areas and 2800 km2 corresponded to areas of standing forests. This type of information and its timely availability are critical for regional and global environmental studies. The results showed that daily MODIS sensor data are useful sources of information for mapping burnt areas, and the proposed method can be used in an operational project in Brazilian Amazonia. [ABSTRACT FROM AUTHOR]

Abundant vegetation species and associated complex forest stand structures in moist tropical regions often create difficulties in accurately classifying land-use and land-cover (LULC) features. This paper examines the value of spectral mixture analysis (SMA) using Landsat Thematic Mapper (TM) data for improving LULC classification accuracy in a moist tropical area in Rondônia, Brazil. Different routines, such as constrained and unconstrained least-squares solutions, different numbers of endmembers, and minimum noise fraction transformation, were examined while implementing the SMA approach. A maximum likelihood classifier was also used to classify fraction images into seven LULC classes: mature forest, intermediate secondary succession, initial secondary succession, pasture, agricultural land, water, and bare land. The results of this study indicate that reducing correlation between image bands and using four endmembers improve classification accuracy. The overall classification accuracy was 86.6% for the seven LULC classes using the best SMA processing routine, which represents very good results for such a complex environment. The overall classification accuracy using a maximum likelihood approach was 81.4%. Another finding is that use of constrained or unconstrained solutions for unmixing the atmospherically corrected or raw Landsat TM images does not have significant influence on LULC classification performances when image endmembers are used in a SMA approach. [ABSTRACT FROM AUTHOR]

Due to the El Niño phenomenon, the 1997-1998 dry season in Roraima (Brazil, Amazonia) was particularly pronounced. Consequently, vegetation fires spread widely and were monitored by many satellites in real time. Satellite images are currently being used to monitor vegetation fires either globally for climate studies or more regionally for impact assessment. After reviewing different satellite data used for impact assessment, this paper focuses on the contribution of SPOT-4's imagery provided by high resolution HRVIR and coarse resolution VEGETATION sensors. These sensors are described with emphasis on those characteristics of potential benefit for forest mapping and fire detection. Early images of Roraima from SPOT-4 are analysed and interpreted to delineate the areas already damaged by fire. VEGETATION images provide a first estimate of damaged areas on a regional scale and an indication of the main ecosystems affected. SPOT HRVIR is used to establish a much more precise classification of various ecosystems. Each vegetation class is associated with a biomass density. From the known burned areas, an estimate of burned biomass during the 1998 dry season is computed, as well as total carbon release. On an intensive study site of 20 400 km[sup 2], 3060 km[sup 2] of savannahs and crops and 6980 km[sup 2] of forest have been burned; the corresponding carbon release is estimated as 210 000 t for croplands and savannahs and 23 M t for the evergreen seasonal forest. The estimated burnt surface areas derived from VEGETATION are then cross-validated with HRVIR and thus an attempt is made to extrapolate the burned biomass with the help of VEGETATION on a regional scale. [ABSTRACT FROM AUTHOR]

High deforestation rates in Amazonia have motivated considerable efforts to monitor forest changes with satellite images, but mapping forest distribution and monitoring change at a regional scale remain a challenge. This article proposes a new approach based on the integrated use of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM) images to rapidly map forest distribution in Rondônia, Brazil. The TM images are used to differentiate forest and non-forest areas and the MODIS images are used to extract three fraction images (vegetation, shade and soil) with linear spectral mixture analysis (LSMA). A regression model is built to calibrate the MODIS-derived forest results. This approach is applied to the MODIS image in 2004 and is then transferred to other MODIS images. Compared to INPE PRODES (Brazil's Instituto Nacional de Pesquisas Espaciais – Programme for the Estimation of Deforestation in the Brazilian Amazon) data, the errors for total forest area estimates in 2000, 2004 and 2006 are −0.97%, 0.81% and −1.92%, respectively. This research provides a promising approach for mapping fractional forest (proportion of forest cover area in a pixel) distribution at a regional scale. The major advantage is that this procedure can rapidly provide the spatial and temporal patterns of fractional forest cover distribution at a regional scale by the integrated use of MODIS images and a limited number of Landsat images. [ABSTRACT FROM AUTHOR]

Analysis of spectral features of vineyards was performed by using satellite images and field radiometry. The possibility of identification of grape variety was investigated. Multispectral satellite images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor were used to study two regions in the Rio Grande do Sul State, Brazil. These regions are 500 km apart and have significant differences in relief, soil and climate, and in the culture associated with vine-tending and wine-making. Applying the concept of 'terroir' as a geographical unit whose specific descriptors transmit an identity to the products coming from it, both regions were considered to be distinct terroirs, with respect to viticulture. For one terroir, with plots of up to 10 ha and vines in rows, spectral information from nine ASTER bands is given for 13 Vitis vinifera varieties. For the second terroir, which has smaller plots and vines supported by continuous trelliswork, radiometric field measurements provided spectral information in addition to ASTER data. The results are that the vineyards are positively identified in the ASTER images; the satellite image data are compatible with field radiometric measurements, in the sense that the grape varieties observed with both methods show reflectances with similar behaviour; the spectra of a given grape variety, taken from three different plots, are mutually consistent; parcels of white and red grape varieties can be separated into classified orbital images; and the spectral information is influenced by the terroir. It is concluded that ASTER images are a valuable tool for vineyard observation and monitoring, applied to area measurements and grape variety identification, leading to multiple applications, including precision viticulture. [ABSTRACT FROM AUTHOR]

The Restinga of Marambaia is an emerged sand bar located between the Sepetiba Bay and the South Atlantic Ocean, on the south-east coast of Brazil. The objective of this study was to observe the geomorphologic evolution of the coastal zone of the Restinga of Marambaia using multitemporal satellite images acquired by multisensors from 1975 to 2004. The images were digitally segmented by a region growth algorithm and submitted to an unsupervised classification procedure (ISOSEG) followed by a raster edit based on visual interpretation. The image time-series showed a general trend of decrease in the total sand bar area with values varying from 80.61 km2 in 1975 to 78.15 km2 in 2004. The total area calculation based on the 1975 and 1978 Landsat MSS data was shown to be super-estimated in relation to the Landsat TM, Landsat ETM+, and CBERS-2 CCD data. These differences can also be associated to the relatively poorer spatial resolution of the MSS data, nominally 79 m, against the 20 m of the CCD data and 30 m of the TM and ETM+ data. For the estimates of the width in the central portion of the sand bar the variation was from 158 m (1975) to 100 m (2004). The formation of a spit in the northern region of the study area was visually observed. The area of the spit was estimated, with values varying from 0.82 km2 (1975) to 0.55 km2 (2004). [ABSTRACT FROM AUTHOR]

Deforestation in Rondônia state in the south-western part of the Brazilian Legal Amazon was analysed using Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), National Oceanic & Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and hydrological data. The Landsat sensor data coverage was supplemented with Pathfinder AVHRR Land (PAL) Normalized Difference Vegetation Index (NDVI) datasets. The results from the Landsat-based analysis show that more than 30% of the natural vegetation in the study area was subject to deforestation between 1973 and 1999, a finding reinforced by analysis of the PAL NDVI data. In addition, it was established that trends in the PAL NDVI datasets were coincident with the pattern of deforestation. Apart from imagery analysis, time variations in the hydrological data between 1982 and 1988 were used to estimate the evapotranspiration. A decreasing trend was identified in the rate of evapotranspiration, suggesting that deforestation has a significant impact on the local hydrological cycle. [ABSTRACT FROM AUTHOR]