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The reflectance spectra of vegetation carry potentially useful information that can be used to determine chemical composition and discriminate between vegetation classes. If compared with analytical methods such as conventional chemical analysis, reflectance measurement provides non-destructive, economic, near real-time data. This paper reports results from reflectance measurements performed by spectroradiometry on leaves and branches of Vitis vinifera L. cv. Merlot and Cabernet Sauvignon from two vineyards in south Brazil. The vineyards had different geological origins but were subjected to the same management. The objectives were to detect spectral differences between the vineyards, and to correlate these differences to variations in foliar traits like the chemical composition of vine leaves. To that end, seven vine parcels were selected for reflectance measurements and chemical analyses (of eleven elements) of vine leaves, and correlations between reflectance and chemical composition were looked for. An initial investigation by discriminant analysis applied to reflectance data of leaves and branches and to grape varieties as well allowed for good separation between vineyards and varieties (> 90% accuracy). By further investigating the correlations between leaf chemical composition and reflectance along the wavelength domain covered by the measurements, we found several well-determined wavelengths with Pearson correlation coefficients r > 0.7. Concentrations of elements could be modelled up to 94% accuracy. These preliminary results, which have to be validated, suggest that variations in soil properties induce chemical differences in vine leaves that can be detected by reflectance measurements. Applications of this observation include the assessment of the chemical content of vine leaves by spectroradiometry as a fast, low-cost alternative to chemical analytical methods. [ABSTRACT FROM AUTHOR]

Land cover, an important factor for monitoring changes in land use and erosion risk, has been widely monitored and evaluated by vegetation indices. However, a study that associates normalized difference vegetation index (NDVI) time series to climate parameters to determine soil cover has yet to be conducted in the Atlantic Rainforest of Brazil, where anthropogenic activities have been carried out for centuries. The objective of this paper is to evaluate soil cover in a Brazilian Atlantic rainforest watershed using NDVI time series from Thematic Mapper (TM) Landsat 5 imagery from 1986 to 2009, and to introduce a new method for calculating the cover management factor (C-factor) of the Revised Universal Soil Loss Equation (RUSLE) model. Twenty-two TM Landsat 5 images were corrected for atmospheric effects using the 6S model, georeferenced using control points collected in the field and imported to a GIS database. Contour lines and elevation points were extracted from a 1:50,000-scale topographic map and used to construct a digital elevation model that defined watershed boundaries. NDVI and RUSLE C-factor values derived from this model were calculated within watershed limits with 1 km buffers. Rainfall data from a local weather station were used to verify NDVI and C-factor patterns in response to seasonal rainfall variations. Our proposed method produced realistic values for RUSLE C-factor using rescaled NDVIs, which highly correlated with other methods, and were applicable to tropical areas exhibiting high rainfall intensity. C-factor values were used to classify soil cover into different classes, which varied throughout the time-series period, and indicated that values attributed to each land cover cannot be fixed. Depending on seasonal rainfall distribution, low precipitation rates in the rainy season significantly affect the C-factor in the following year. In conclusion, NDVI time series obtained from satellite images, such as from Landsat 5, are useful for estimating the cover management factor and monitoring watershed erosion. These estimates may replace table values developed for specific land covers, thereby avoiding the cumbersome field measurements of these factors. The method proposed is recommended for estimating the RUSLE C-factor in tropical areas with high rainfall intensity. [ABSTRACT FROM PUBLISHER]

This paper provides a comparative analysis of land-use and land-cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired during the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes – forest, savanna, other vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water – was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition, and rates among the three study areas and indicates the importance of analysing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g. urban expansion, roads, and land-use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates. [ABSTRACT FROM AUTHOR]

The objective of this paper is to describe qualitatively channel morphology from the reflectance pattern of high sediment concentrations. The spectral behaviour of the aquatic environment is not only a function of water depth, but also the concentration and spatial distribution of suspended sediments, inorganic and organic, among others. We used Landsat 7/ETM+ (Enhanced Thematic Mapper Plus) images for mapping river channel morphology in Sao Francisco river in Brazil. The methodology was based on: (a) the elaboration of the digital elevation model (DEM) of bathymetric data, (b) the development of an algorithm based on bands ratio index of Landsat7/ETM+ digital image, and (c) the comparison between bathymetry and developed index. Our approach was to develop an index for the emission feature in red band (ETM+ 3 band) considering its height. A difference behaviour in relation to the index and the depth for both symmetric and asymmetric channels was observed. Thus, the river morphology was established, in an indirect way, by considering the spatial distribution of the sedimentary plumes. [ABSTRACT FROM AUTHOR]

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]

This paper proposes a method to support sugarcane yield forecast using vegetation spectral indices, principal component analysis and historic yield data. The study area is located in the State of São Paulo, Brazil, and is divided into 11 production plots (108.75 ha), where sugarcane of the RB85 5536 variety is cultivated on red latossol (oxissol‐type) soil and flat topography. The data employed in the study include radiometrically and geometrically corrected enhanced thermatic mapper Plus (ETM+)/Landsat‐7 and ASTER/Terra images, acquired in June and April 2001, respectively, and historic harvest data measured in 2000 and 2001. The method comprises several steps: (a) enhancement of specific spectral responses of vegetation constituents; (b) reduction of spectral dimensions with prioritization of information and weighing of parameters related to foliar area; the data processed through these steps are reduced to a single image (the synthesis image), from which the mean DN (digital number) per cultivated area is calculated; (c) the image DNs are subsequently transformed into ton of stalk per hectare (t ha-1) through normalization, which requires knowledge of the previous year's yield for the cultivated production plots under analysis. Yield estimates using the method showed greater precision in comparison to the ubiquitous visual methods employed by the sugarcane agro‐industry in Brazil. Using factual productivity data of the year 2000 harvest only, the method achieved estimate errors varying between 2.57% and 5.65%, compared with 9.06% expected by the sugar factory; whereas using data from the year 2001 harvest, error margins were remarkably lower, around 1%. [ABSTRACT FROM AUTHOR]

This paper presents a feasibility study using multi-temporal Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data to classify sugarcane crop. This study was carried out in São Paulo State which is the major sugarcane producer in Brazil, occupying more than 3.1 million hectares. Cloud-free MODIS images (16 days mosaics) were acquired over a period of almost 15 months. Samples of sugarcane and non-sugarcane were randomly selected and cluster analysis was performed to establish similar EVI temporal behaviour clusters. It was observed that EVI was sensitive to variations in land-use cover mainly du to phenology and land management practices. Therefore, selection of sugarcane samples with similar EVI temporal behaviour for supervised classification was difficult due to both large planting and large harvesting periods. Consequently, cluster analysis was chosen to carry out an unsupervised classification. The best results were obtained in regions occupied by: natural and planted forest, soybean, peanuts, water bodies and urban areas which contrasted with the temporal-spectral behaviour of sugarcane. The lowest performance was observed mainly in regions dominated by pasture, which has similar temporal-spectral behaviour to sugarcane. This study provided useful information to define a MODIS image classification procedure for sugarcane crop for the whole State area based on the large amount of cloud-free MODIS images when compared with other currently available optical sensors. [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]

This paper presents the methodology used to build a Landsat Thematic Mapper (TM) digital mosaic for the Amazon River mainstem floodplain. Twenty-nine almost cloud-free TM Landsat scenes covering a period from 1985 to 1995 were selected from the National Institute for Space Research (INPE) archive. Most of the scenes were acquired from July to September, a period that begins with high waters and ends with receding water up to about the beginning of the low waters. Radiometric rectification was applied to the images to reduce variability of environmental conditions during Landsat TM data acquisition. The radiometric rectification applied had a good performance for bands 3, 5, and 7, for most of the scenes. For bands 1 and 2 the radiometric rectification was limited, especially for scenes with intense haze. Nevertheless, the overall performance of radiometric normalization allowed the production of a uniform dataset for the entire Brazilian Amazon River mainstem floodplain. [ABSTRACT FROM AUTHOR]