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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]

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]

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]

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]

Remotely sensed data are the best and perhaps the only possible way for monitoring large-scale, human-induced land occupation and biosphere-atmosphere processes in regions such as the Brazilian tropical savanna (Cerrado). Landsat imagery has been intensively employed for these studies because of their long-term data coverage (>30 years), suitable spatial and temporal resolutions, and ability to discriminate different land-use and land-cover classes. However, cloud cover is the most obvious constraint for obtaining optical remote sensing data in tropical regions, and cloud cover analysis of remotely sensed data is a requisite step needed for any optical remote sensing studies. This study addresses the extent to which cloudiness can restrict the monitoring of the Brazilian Cerrado from Landsat-like sensors. Percent cloud cover from more than 35 500 Landsat quick-looks were estimated by the K-means unsupervised classification technique. The data were examined by month, season, and El Niño Southern Oscillation event. Monthly observations of any part of the biome are highly unlikely during the wet season (October-March), but very possible during the dry season, especially in July and August. Research involving seasonality is feasible in some parts of the Cerrado at the temporal satellite sampling frequency of Landsat sensors. There are several limitations at the northern limit of the Cerrado, especially in the transitional area with the Amazon. During the 1997 El Niño event, the cloudiness over the Cerrado decreased to a measurable but small degree (5% less, on average). These results set the framework and limitations of future studies of land use/land cover and ecological dynamics using Landsat-like satellite sensors. [ABSTRACT FROM AUTHOR]

This study addresses the identification of sugarcane varieties using data from an orbital‐borne sensor in an attempt to reduce evaluation time and field‐checking efforts. It would help institutions that breed sugarcane varieties for royalties charges for the propagation of their genetic material. The approach of this work was to apply a methodology to discriminate sugarcane varieties through Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The hypothesis stated is that varieties have different canopies due to their particular morpho‐physical characteristics, hence it might be possible to identify such differences through orbital spectral data. Thus, an image of a sugarcane‐producing area in western São Paulo State, Brazil was obtained and first cut fields sugarcane of four varieties were evaluated. The discrimination techniques tested were: analysis of individual bands, pixel dispersion plots and discriminating equations. In single‐band analysis it was found that the near‐infrared (NIR) band is the most appropriate. The observation of the pixel dispersion plots between red vs NIR and NIR vs GreenNDVI also helps in the discrimination of these varieties. We concluded that it is possible to identify sugarcane varieties through the discriminating equations with 93.6% of certainty. [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]