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.

The objective of this work was to evaluate the seasonal variation of soil cover and rainfall erosivity, and their influences on the revised universal soil loss equation (Rusle), in order to estimate watershed soil losses in a temporal scale. Twenty-two TM Landsat 5 images from 1986 to 2009 were used to estimate soil use and management factor (C factor). A corresponding rainfall erosivity factor (R factor) was considered for each image, and the other factors were obtained using the standard Rusle method. Estimated soil losses were grouped into classes and ranged from 0.13 Mg ha^-1 on May 24, 2009 (dry season) to 62.0 Mg ha^-1 on March 11, 2007 (rainy season). In these dates, maximum losses in the watershed were 2.2 and 781.5 Mg ha^-1 , respectively. Mean annual soil loss in the watershed was 109.5 Mg ha^-1 , but the central area, with a loss of nearly 300.0 Mg ha^-1 , was characterized as a site of high water-erosion risk. The use of C factor obtained from remote sensing data, associated to corresponding R factor, was fundamental to evaluate the soil erosion estimated by the Rusle in different seasons, unlike of other studies which keep these factors constant throughout time.

Information about rainfall erosivity is important during soil and water conservation planning. Thus, the spatial variability of rainfall erosivity of the state Mato Grosso do Sul was analyzed using ordinary kriging interpolation. For this, three pluviograph stations were used to obtain the regression equations between the erosivity index and the rainfall coefficient EI30. The equations obtained were applied to 109 pluviometric stations, resulting in EI30 values. These values were analyzed from geostatistical technique, which can be divided into: descriptive statistics, adjust to semivariogram, cross-validation process and implementation of ordinary kriging to generate the erosivity map.Highest erosivity values were found in central and northeast regions of the State, while the lowest values were observed in the southern region. In addition, high annual precipitation values not necessarily produce higher erosivity values.

Desenvolveu-se este trabalho com o objetivo de determinar os diferentes padrões de chuvas erosivas naturais, calcular os índices de erosividade EI30 e KE > 25 e ajustá-los aos dados mensais de precipitação para as regiões de Seropédica e Nova Friburgo, RJ. Com os resultados obtidos conclui-se que o padrão de chuva avançado é o de maior ocorrência para as duas regiões, correspondendo a 61 e 58% do número total das chuvas analisadas seguido, respectivamente, pelos padrões intermediário, com 24 e 24%, e atrasado, com 15 e 18%; os valores médios anuais de EI30 foram de 5.472,5 e de 5.431,2 MJ mm ha-1 h-1, para Seropédica e Nova Friburgo, respectivamente, enquanto os valores médios anuais de K E> 25 foram, respectivamente, de 76,8 e 83,5 MJ ha-1.

Desenvolveu-se este trabalho com o objetivo de analisar a variabilidade espacial da erosividade no Estado do Rio de Janeiro, por meio de análise geoestatística. Os índices de erosividade médios anuais EI30, definidos pelo produto da energia cinética da chuva e sua intensidade máxima em 30 min e KE>25 (definidos como a energia cinética das chuvas com intensidades superiores a 25 mm h-1) foram calculados a partir de dados pluviográficos de 36 estações, enquanto, para outras 57 estações, os mesmos índices foram estimados por meio de equações de regressão, totalizando 93 pontos de amostragem. O modelo matemático ajustado ao semivariograma experimental, para ambos os índices, foi o exponencial. A partir dos parâmetros dos modelos ajustados, foi possível gerar mapas de erosividade pelo método da krigagem, que apresenta vantagens em relação aos métodos convencionais. Além disso, também foram gerados mapas de variância de krigagem. Os maiores valores de erosividade foram observados nas regiões Serrana e da Baía da Ilha Grande, enquanto os menores valores foram observados nas regiões norte e noroeste do Estado. As maiores variâncias de krigagem foram observadas nas regiões Litorânea e Norte, que são as que apresentam menores densidades de amostragem.