NDVI time series for monitoring RUSLE cover management factor in a tropical watershed

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.